With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an Intrusion Detection System is very important. Hence there is high demand to reduce the threat level in networks to ensure the data and services offered by them to be more secure. In this paper we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection system using the layered CRFs. We set up different types of checks at multiple levels in each layer. Our framework examines various attributes at every layer in order to effectively identify any breach of security. Once the attack is detected, it is intimated through mobile phone to the system administrator for safeguarding the server system. We established experimentally that the layered CRFs can thus be more effective in detecting intrusions when compared with the other previously known techniques.
- Wireless sensor networks are vulnerable to security attacks due to their distributed nature, multi-hop communication, and lack of resources. Intrusion detection systems play an important role in detecting attacks.
- There are three main types of intrusion detection systems: signature-based, anomaly-based, and specification-based (a hybrid of the two). Signature-based systems detect known attacks but miss new ones, while anomaly-based systems can detect new attacks but have high false positives.
- The paper compares these intrusion detection systems for wireless sensor networks and finds that anomaly-based systems have the lowest resource usage but may miss known attacks, while signature-based systems detect known attacks but use more resources. The best approach
This document summarizes an international journal on information technology and management information systems. It discusses detecting and classifying attacks in a computer network. Existing approaches to intrusion detection include anomaly-based systems, host-based intrusion detection systems (HIDS), and network-based intrusion detection systems (NIDS). A multilayer perceptron (MLP) algorithm is commonly used for intrusion detection but has limitations. The paper proposes a modified apriori algorithm to generate rules for detecting and classifying attacks into categories and types to enable recommending appropriate responses.
A technical review and comparative analysis of machine learning techniques fo...
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICS
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Machine learning in network security using knime analytics
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHM
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
This document summarizes a research paper on adaptive personalized web search with safety seclusion. It discusses how personalized web search has improved search quality but user privacy concerns have limited its adoption. The paper proposes a system called UPS that can dynamically generalize user profiles during searches while respecting indicated privacy requirements. UPS uses greedy algorithms to balance personalization utility and privacy risk from exposing generalized profiles. The system aims to address limitations in existing personalized search regarding user security and accuracy needs.
Survey of Clustering Based Detection using IDS Technique
This document discusses intrusion detection systems (IDS) and different techniques used for IDS, including clustering-based detection. It first provides background on IDS, describing their purpose of detecting intruders and protecting systems. It then outlines various IDS types, including mobile agent-based, cluster-based, cryptography-based, and others. The document also summarizes related work from other papers applying data mining techniques like clustering to improve IDS detection rates and reduce false alarms. Finally, it discusses problems with current and traditional IDS, such as threshold detection leading to false positives, and false negatives where attacks are missed.
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
This document summarizes various soft computing techniques that can be used for intrusion detection, including fuzzy logic, graph-based approaches, and neural networks. Fuzzy logic can be used to classify parameters and detect anomalies by comparing normal and new fuzzy association rule sets. Graph-based approaches model network traffic as graphs of nodes and edges and use clustering algorithms to detect anomalies. Neural networks can be trained on audit log data to recognize normal behavior and detect deviations that may indicate attacks. These soft computing methods aim to improve on signature-based detection by learning patterns of normal network activity and detecting anomalies.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
A Performance Analysis of Chasing Intruders by Implementing Mobile AgentsCSCJournals
This document summarizes a research paper that proposes using mobile agents to improve intrusion detection systems. The paper presents an architecture for an intrusion detection system that uses mobile agents to autonomously collect intrusion-related information from systems on a network. Information collector agents gather data, while chasing agents work to trace the path of intrusions and locate their origin. The paper evaluates this approach and discusses how mobile agents can enhance intrusion detection through their mobility and autonomous functionality.
- Wireless sensor networks are vulnerable to security attacks due to their distributed nature, multi-hop communication, and lack of resources. Intrusion detection systems play an important role in detecting attacks.
- There are three main types of intrusion detection systems: signature-based, anomaly-based, and specification-based (a hybrid of the two). Signature-based systems detect known attacks but miss new ones, while anomaly-based systems can detect new attacks but have high false positives.
- The paper compares these intrusion detection systems for wireless sensor networks and finds that anomaly-based systems have the lowest resource usage but may miss known attacks, while signature-based systems detect known attacks but use more resources. The best approach
This document summarizes an international journal on information technology and management information systems. It discusses detecting and classifying attacks in a computer network. Existing approaches to intrusion detection include anomaly-based systems, host-based intrusion detection systems (HIDS), and network-based intrusion detection systems (NIDS). A multilayer perceptron (MLP) algorithm is commonly used for intrusion detection but has limitations. The paper proposes a modified apriori algorithm to generate rules for detecting and classifying attacks into categories and types to enable recommending appropriate responses.
A technical review and comparative analysis of machine learning techniques fo...IJECEIAES
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
This document summarizes a research paper on adaptive personalized web search with safety seclusion. It discusses how personalized web search has improved search quality but user privacy concerns have limited its adoption. The paper proposes a system called UPS that can dynamically generalize user profiles during searches while respecting indicated privacy requirements. UPS uses greedy algorithms to balance personalization utility and privacy risk from exposing generalized profiles. The system aims to address limitations in existing personalized search regarding user security and accuracy needs.
Survey of Clustering Based Detection using IDS Technique IRJET Journal
This document discusses intrusion detection systems (IDS) and different techniques used for IDS, including clustering-based detection. It first provides background on IDS, describing their purpose of detecting intruders and protecting systems. It then outlines various IDS types, including mobile agent-based, cluster-based, cryptography-based, and others. The document also summarizes related work from other papers applying data mining techniques like clustering to improve IDS detection rates and reduce false alarms. Finally, it discusses problems with current and traditional IDS, such as threshold detection leading to false positives, and false negatives where attacks are missed.
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
An Extensive Survey of Intrusion Detection SystemsIRJET Journal
This document summarizes an extensive survey of intrusion detection systems. It discusses the general architecture of IDS, including host-based and network-based systems. It describes different types of attacks (e.g. DoS, probing, user-to-root) and defenses. It analyzes previous work applying data mining techniques like machine learning to improve detection rates and reduce false alarms. A key problem is the massive number of false alarms that overburden security managers; the document aims to investigate solutions to lower the false alarm rate so that real threats are not missed.
A Survey: Comparative Analysis of Classifier Algorithms for DOS Attack Detectionijsrd.com
In today's interconnected world, one of pervasive issue is how to protect system from intrusion based security attacks. It is an important issue to detect the intrusion attacks for the security of network communication.Denial of Service (DoS) attacks is evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money.Significance of Intrusion detection system (IDS) in computer network security well proven. Intrusion Detection Systems (IDSs) have become an efficient defense tool against network attacks since they allow network administrator to detect policy violations. Mining approach can play very important role in developing intrusion detection system. Classification is identified as an important technique of data mining. This paper evaluates performance of well known classification algorithms for attack classification. The key ideas are to use data mining techniques efficiently for intrusion attack classification. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.
IRJET - A Secure Approach for Intruder Detection using BacktrackingIRJET Journal
This document summarizes a research paper that proposes a secure approach for intruder detection using backtracking. The approach detects intruders by analyzing network traffic and matching it to known attack patterns. If an abnormal behavior or attack is identified, an alert is sent to the administrator. When messages are sent between nodes, the receiver uses backtracking to check the transaction history and identify any differences in node keys that could indicate an intruder. This helps track down the intruder by analyzing previous transactions in the network.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
This document proposes an intrusion detection system using customized rules for the Snort tool to improve security. The system uses Wireshark to scan network traffic for anomalies, Snort to detect attacks using customized rulesets for faster response times, and Wazuh and Splunk to analyze log files. Rules are created using the Snorpy tool and added to Snort to monitor for specific attacks like ICMP ping impersonation and authentication attempts. When attacks are attempted, the system successfully detects them and logs the alerts. The integration of these tools provides low-cost intrusion detection capabilities with automated threat identification and faster response compared to existing Snort configurations.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
This document summarizes an international journal on information technology and management information systems. It discusses detecting and classifying attacks in a computer network. Existing approaches to intrusion detection include anomaly-based systems, host-based intrusion detection systems (HIDS), and network-based intrusion detection systems (NIDS). A multilayer perceptron (MLP) algorithm is commonly used for intrusion detection but has limitations. The paper proposes a modified apriori algorithm to generate rules for detecting and classifying attacks into categories and types to enable recommending appropriate responses.
This document summarizes an article that proposes integrating conditional random fields (CRFs) and a layered approach to improve intrusion detection systems. CRFs can effectively model relationships between different features to increase attack detection accuracy. A layered approach reduces computation time by eliminating communication overhead between layers and using a small set of features in each layer. The proposed system aims to achieve both high attack detection accuracy using CRFs and high efficiency using the layered approach. It presents integrating these two methods for intrusion detection to address issues with limited coverage, high false alarms, and inefficiency in existing systems.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
This document summarizes a proposed network attack alerting system that aims to reduce the large number of alerts generated by intrusion detection systems (IDS). The system uses both network-based and host-based IDS to detect attacks launched using the Backtrack attacking tools on a virtual network lab environment. Well-known open source security tools on the Security Onion Linux distribution are used to generate alerts. The system defines rules to identify important alert types and stores alerts in a database. It aims to eliminate redundant alerts for the same attack by analyzing attributes like source/destination IP and port. Alert severity levels are defined using threshold counts and times to classify alerts and help administrators respond appropriately.
This document summarizes a proposed network attack alerting system that aims to reduce redundant alerts from intrusion detection systems (IDS). The system uses both network-based and host-based IDS to detect attacks launched using the Backtrack penetration testing tool on a virtual network environment. Well-known open source IDS tools from the Security Onion distribution are used to generate alerts. The system builds a database of alerts and defines rules to eliminate duplicate alerts for the same attack based on attributes like source/destination IP and port. It also establishes a severity classification scheme using threshold values of alerts and time to help administrators prioritize responses.
INTERNAL SECURITY ON AN IDS BASED ON AGENTScscpconf
The document describes a proposed hierarchical intrusion detection system (IDS) based on agents. Key points:
1. The IDS uses a multi-agent approach with different agent types (collectors, transceivers, monitors) to distribute monitoring tasks without affecting system performance.
2. Internal security techniques are implemented to verify the identity and integrity of agents, such as using a matrix of marks and hash functions to check agents.
3. The IDS was prototyped using the BESA multi-agent platform and tested for its ability to securely detect intrusions in an agent-based system.
Internal security on an ids based on agentscsandit
The document describes a proposed hierarchical intrusion detection system (IDS) based on agents. Key points:
1. The IDS uses a multi-agent approach with different agent types (collectors, transceivers, monitors) to distribute monitoring tasks without affecting system performance.
2. Internal security techniques are implemented to verify the identity and integrity of agents, such as using a matrix of marks and hash functions to check agents.
3. The IDS was prototyped using the BESA multi-agent platform and can detect attacks through signature matching and event correlation across the agent network.
INTERNAL SECURITY ON AN IDS BASED ON AGENTSIJNSA Journal
An Intrusion Detection System (IDS) can monitor different events that may occur in a determined network or host, and which affect any network security service (confidentiality, integrity, availability). Because of this, an IDS must be flexible and it must detect and trace each alert without affecting the system´s performance. On the other hand, agents ina Multi-Agent system have inherent security problems due to their mobility; that’s why we propose some techniques in order to provide internal security for the agents belonging to the system. The deployed IDS works with a multiagent platform and each component inside the infrastructure is verified using security techniques in order to provide integrity. Likewise, the agents can specialize in order to carry out specific jobs, for example monitoring TCP, UDP traffic, etc. The IDS can work without interfering in the system's performance. In this article we present a hierarchical IDS deployment with internal security on a multiagent system, using a platform named BESA with its processes, functions and results.
INTERNAL SECURITY ON AN IDS BASED ON AGENTSIJNSA Journal
This document describes an intrusion detection system (IDS) based on a multi-agent system that provides internal security for agents. The IDS uses different types of agents (collectors, transceivers, itinerants, monitors) organized in a hierarchical structure to monitor network traffic and detect intrusions. The system implements techniques like a matrix of marks and hash functions to verify the identity and integrity of agents and prevent attacks from within the platform. The IDS architecture is presented along with how the agents communicate and coordinate to correlate events, generate new signatures, and ensure internal security without affecting system performance. The document evaluates this agent-based IDS approach for providing security while maintaining flexibility and adaptability.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Articles - International Journal of Network Security & Its Applications (IJNSA)IJNSA Journal
International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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A Review Of Intrusion Detection System In Computer NetworkAudrey Britton
This document provides an overview of intrusion detection systems (IDS) and the techniques used to implement them. It discusses that IDS are used to detect malicious actions on computer networks and protect important files and documents. The document then summarizes that IDS have four main components - sensors to monitor the system, a database to store event information, an analysis module to detect potential threats, and a response module to address detected threats. It also categorizes IDS based on the data source, detection approach, structure, and how intrusions are detected. Finally, the document outlines various techniques used in IDS, including artificial intelligence methods like neural networks, fuzzy logic, genetic algorithms and machine learning approaches.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
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IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BASED ON DEVIANT SYSTEM BEHAVIOUR
1. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
DOI : 10.5121/ijnsa.2013.5214 179
IMPROVED IDS USING LAYERED CRFS WITH
LOGON RESTRICTIONS AND MOBILE ALERTS
BASED ON DEVIANT SYSTEM BEHAVIOUR
Arpitha M1
, Geetha V1
,
Gowranga K H2
and Bhakthavathsalam R2
1
Department of Information Science and Engineering
Alpha College Of Engineering, Bangalore, India
arpitha119@gmail.com, geethaanjali78@gmail.com
2
Supercomputer Education and Research Center
Indian Institute of Science, Bangalore, India
gowranga@serc.iisc.ernet.in, bhaktha@serc.iisc.ernet.in
ABSTRACT
With the ever increasing numberand diverse type of attacks, including new and previouslyunseen attacks,
the effectiveness of an Intrusion DetectionSystem is very important. Hence there is high demand to reduce
the threat level in networks to ensure the data and services offered by them to be more secure. In this paper
we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection
system using the layered CRFs. We set up different types of checks at multiple levels in each layer.Our
framework examines various attributes at every layer in order to effectively identify any breach of security.
Once the attack is detected, it is intimated throughmobile phone to the system administrator for
safeguarding the server system. We established experimentally that the layered CRFs can thus be more
effectivein detecting intrusions when compared with the other previouslyknown techniques.
KEYWORDS
Network Security, Intrusion Detection, Layered Approach, Conditional Random Fields, Mobile Phones
1. INTRODUCTION
The current state of network is vulnerable they are prone to increasing number of attacks. Thus
securing a network from unwanted malicious traffic is of prime concern. A computer network
needs to provide continuous services, such as e-mail to users, while on the other it stores huge
amount of data which is of vital significance. Recently, there has been increasing concern over
safeguarding the vast amount of data stored in a network from malicious modifications and
disclosure to unauthorized individuals. Intrusion Detection Systems (IDS) [1] are based on two
concepts; matching of the previously seen and hence known anomalous patterns from an internal
2. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
180
database of signatures or building profiles based on normal data and detecting deviations from the
expected behaviour[2].Based on the mode of deployment, the Intrusion Detection Systems are
classifiedas Network based [3] andHost based [4]. Network based systems make a decision by
analysing the network logs and packet headers from the incoming and outgoing packets. Host
based systems monitor's individual systems and uses system logs extensively to make any
decision. Intrusion Detection Systems are either Signature based or Behaviour based [5]. The
Signature based systems build a model based on the available knowledge of the attacks. The
Behaviour based systems which build a model based on the available knowledge of the normal
use of the system.We propose and evaluate the use of the CRFs [6] also which is a novel
technique for the task of Intrusion Detection along with Layered Approach. Further, our system
can be used as a standalone system monitoring an entire Network or a single Host or even a single
Application running on a particular host.
1.1 Intrusion Detection
Intrusion detection [7] is the process of monitoring computers or networks for unauthorized
entrance, activity, or file modification. An IDS (Intrusion Detection System) is a device or
application used to inspect all network traffic, thereby detecting if a system is being targeted by a
network attack such as a denial of service attack. In some cases the IDS may also respond to
anomalous or malicious traffic by taking action such as blocking the user or source IP address
from accessing the network. IDS protect a network and attempt to prevent intrusions. They don’t
fully guarantee security, but when used with security policy, vulnerability assessments, data
encryption, user authentication, access control, and firewalls, they can greatly enhance network
safety [8].
Intrusion detection systems serve three essential security functions: they monitor, detect, and
respond to unauthorized activity by company insiders and outsiders. Intrusion detection systems
use policies to define certain events that, if detected will issue an alert. Certain intrusion detection
systems have the capability of sending out alerts, so that the administrator of the IDS will receive
a notification of a possible security incident. Many intrusion detection systems not only recognize
a particular incident and issue an appropriate alert, they also respond automatically to the event.
Such a response might include logging off a user, disabling a user account, and launching of
scripts.
2. NEW SCHEME FOR ROBUST IDS
Intrusion detection as a discipline is fairly immature. Commercially available examples of
successful intrusion detection systems are limited, although the state of the art is progressing
rapidly. The whole concept of our paper is to build an intrusion detection system which is very
accurate in detection of request from unknown computers and which is very fast to respond to
such intrusions taking place in system which gives efficiency [9] to the system and intimating the
administrator about the intrusions through the mobile phone. To achieve this system, we have
integrated the properties of conditional random fields and the layered approach.
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2.1 Existing System
There are a number of methods and frameworks been proposed and many systems have been built
to detect intrusions. Various techniques such as association rules [10], clustering, naive Bayes
classifier, support vector machines, genetic algorithms, artificial neural networks, and others have
been applied to detect intrusions. These existing systems suffer from a wide range of problems.
a. The features are limited to the entry level of the packets and require the no. of records to
be large. They tend to produce a large number of rules that increases the system's
complexity.
b. Some methods consider the features independently and are unable to capture the
relationship between different features of a single record. This further degrades the attack
detection strength of the system.
c. Some existing systems are attack specific and hence they would build networks which
rapidly increases as the detection load increases.
2.2 Proposed System
In our proposed system we describe the Layer-based Intrusion Detection System (LIDS) [11]
[12]. The LIDS draws its motivation from what we call as the Airport Security model, where a
number of security checks are performed one after the other in a sequence. Similar to this model,
the LIDS represents a sequential Layered Approach [13] and is based on ensuring availability,
confidentiality, and integrity of data and (or) services over a network.
The goal of using a layered model is to reduce computation and the overall time required to detect
anomalous events. The time required to detect an intrusive event is significant and can be reduced
by eliminating the communication overhead among different layers. We define four layers they
are Probe layer, DoS layer, R2L layer, and U2R layer. Each layer is separately trained with a
small set of features. The layers essentially act as filters that block any anomalous connection,
thereby eliminating the need of further processing at subsequent layers enabling quick response to
intrusion.
The effect of such a sequence of layers is that the anomalous events are identified and blocked as
soon as they are detected. Once the attack is detected, it is intimated through mobile phone to the
system administrator for safe guarding the server system.
We implement the LIDS and select four set of features which reduces the computational time.
Methods such as naive Bayes [14] assume independence among the observed data. To balance
this trade-off, we use the CRFs that are more accurate, though expensive, but we implement the
Layered Approach to improve overall system performance.
Our proposed system, Layered CRFs, performs significantly better than other systems.
4. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
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Figure 1. Proposed System
3. IMPLEMENTATION
Implementation is the stage when the theoretical design is turned out into a working system. Thus
it can be considered to be the most critical stage in achieving a successful new system and in
giving the user, confidence that the new system will work and be effective. The implementation
stage involves careful planning, investigation of the existing system and it’s constraints on
implementation, designing of methods to achieve changeover and evaluation of changeover
methods.
3.1 Layered Approach for Intrusion Detection
Layer-based Intrusion Detection System (LIDS) draws its motivation from what we call as the
Airport Security model, where a number of security checks are performed one after the other in a
sequence. Similar to this model, the LIDS represents a sequential Layered Approach and is based
on ensuring availability, confidentiality, and integrity of data and (or) services over a network.
Figure 2 gives a generic representation of the framework. The goal of using a layered model is to
reduce computation and the overall time required to detect anomalous events. The time required
to detect an intrusive event is significant and can be reduced by eliminating the communication
5. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
183
overhead among different layers. Every layer in the LIDS framework is trained separately and
then deployed sequentially.
Figure 2. Layered Approach for Intrusion Detection
We define four layers that correspond to the four attack groups [15]. They are Probe layer, DoS
layer, R2L layer, and U2R layer. Each layer is then separately trained with features. Feature
selection is significant for Layered Approach. The layers essentially act as filters that block any
anomalous connection, thereby eliminating the need of further processing at subsequent layers
enabling quick response to intrusion. The effect of such a sequence of layers is that the anomalous
events are identified and blocked as soon as they are detected. Hence, we implement the LIDS
and select four set of features for every layer. In many situations, there is a trade-off between
efficiency and accuracy of the system and there can be various avenues to improve system
performance. To balance this trade-off, we use the CRFs that are more accurate, though
expensive, but we implement the Layered Approach to improve overall system performance. The
performance of our proposed system, Layered CRFs is comparable to that of the decision trees
and the naive Bayes, and our system has higher attack detection accuracy.
3.2 Conditional Random Fields for Intrusion Detection
Conditional models are systems that are used to model the conditional distribution [16] over a set
of random variables. Such models have been extensively used in the natural language processing
tasks. Conditional models offer a better framework and can be used to model rich overlapping
features among the visible observations. CRFs are undirected graphical models used for sequence
tagging.
The CRFs have proven to be very successful in such tasks, as they do not make any unwarranted
assumptions about the data. Hence, we explore the suitability of CRFs for intrusion detection.
System may consider features such as “logged in” and “number of file creations.”
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When these features are analyzed individually, they do not provide any information that can aid
in detecting attacks. However, when these features are analyzed together, they can provide
meaningful information.
Figure 3. Conditional Random Field
3.3 Integrating Layered Approach with Conditional Random Fields
A natural choice is to integrate them to build a single system that is accurate in detecting attacks
and efficient in operation.
Probe layer
The probe attacks are aimed at acquiring information about the target network from a source that
is often external to the network. Hence, basic connection level features such as the “duration of
connection” and “source bytes” are significant while features like “number of files creations” and
“number of files accessed” are not expected to provide information for detecting probes.
DoS layer
For the DoS layer, traffic features such as the “percentage of connections having same destination
host and same service” and packet level features such as the “source bytes” and “percentage of
packets with errors” is significant.
R2L layer
The R2L attacks are one of the most difficult to detect as they involve the network level and the
host level features. We therefore select both the network level features such as the “duration of
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185
connection” and “service requested” and the host level features such as the “number of failed
login attempts” among others for detecting R2L attack.
U2R layer (User to Root attacks)
The U2R attacks involve the semantic details that are very difficult to capture at an early stage.
Such attacks are often content based and target an application. Hence, for U2R attacks, we select
features such as “number of file creations” and “number of shell prompts invoked,” while we
ignored features such as “protocol” and “source bytes.”
Figure 4. Integrating Layered Approach with Conditional Random Fields
3.4 Time Scheduling of Users
With the increasing number of user’s everyday on the internet, networks are getting burdened
with a huge amount of requests, processes, services etc. Every user performs some or the other
function when they are using the internet this increases the load on the network. In our system we
have scheduled a particular day and time for the users who are a part of an organisation,
restricting their usage to prevent intrusions and wastage of bandwidth in the network. In simple
words they are assigned a particular day and time to login to their accounts and work on their
requirements.
We have symbolised the days of a week as 0-6 depicting Sunday-Saturday and time on a 24 hour
clock. This feature is added in the database and access will be given only to those users who login
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186
at the right schedule. Users who do not login at the right schedule are denied access and will be
treated as intruders.
3.5 Intrusion Detected Message Sent to System Administrators Mobile
The mobile device can be used to keep oneself informed about the attacks. The corresponding
error messages are generated and are intimated to the server which schedules the appropriate
actions. Mobile alerts are sent to the server administrator’s mobile through usage of a GSM
modem connected to the COM port of your computer and making sure that the Java
communication API is installed in your system. We also carefully consider several parameters
such as text message centre number found in your mobile in the SMS settings menu and the baud
rate and type of flow control for receiving, type of flow control for sending, the number of data
bits, the number of stop bits, and the type of parity.
In a nutshell, intrusion detection systems do exactly as the name suggests: they detect possible
intrusions. More specifically, IDS tools aim to detect computer attacks and/or computer misuse,
and to alert the proper individuals upon detection.
Through various methods, both detect when an intruder/attacker/burglar is present, and both
subsequently issue some type of warning or alert. Using the previous example, firewalls can be
thought of as a fence or a security guard placed in front of a house. They protect a network and
attempt to prevent intrusions, while IDS tools detect whether or not the network is under attack or
has, in fact, been breached. IDS tools thus form an integral part of a thorough and complete
security system.
Figure 5. Proposed IDS system Activities
Intrusion detection systems [17] serve three essential security functions: they monitor, detect, and
respond to unauthorized activity by company insiders and outsider intrusion. An IDS installed on
9. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
187
a network provides much the same purpose as a burglar alarm system installed in a house.
Intrusion detection systems use policies to define certain events that, if detected will issue an
alert. In other words, if a particular event is considered to constitute a security incident, an alert
will be issued if that event is detected. Many intrusion detection systems not only recognize a
particular incident and issue an appropriate alert, they also respond automatically to the event.
Such a response might include logging off a user, disabling a user account, and launching of
scripts. Our system has the capability of sending out alerts, so that the administrator of the IDS
will receive a notification of a possible security incident in the form of a page, email, or SNMP
trap [18].
3.6 Proposed Algorithm
Step 1: Select the number of layers, n, for the complete system.
Step 2: Separately perform features selection for each layer.
Step 3: Plug in the layers sequentially such that only the connections labelled as normal are
passed to the next layer
Step 4: For each (next) test instance perform Steps 5 through 8.
Step 5: Test the instance and label it either as attack or normal.
Step 6: If the instance is labelled as an attack, block it and then identify it as an attack with the
corresponding layer name at which it is detected and go to step 4. Pass the sequence to
next layer.
Step 7:If the current layer is not the last layer in the system, test the instance and go to step 6.
Else go to step 8.
Step 8: Test the instance and label it either as normal or as an attack. If the instance is labelled as
an attack, block it and identify it as an attack corresponding to the layer name.
Step 9: If the instance is labelled as an attack at any layer then intimate it to system admin’s
mobile with a corresponding appropriate message of attack.
4. RESULTS
We have represented the results for every operation that is performed as per the proposed
algorithm. Our results confirm that the implementations that are carried out are
effectively displaying the outcomes accurately.
We have produced results for eight possible conditions on the use of four CRFs. type of a
system is very much suited in an organizational network. Finally, our system has the advantage
that the number of layers can be increased or decreased depending upon the environment in which
the system is deployed, giving flexibility to the network administrator.
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188
Figure 6. Sequence of checks for Valid User.
Figure 7. Intrusion detected at User Level
For every valid user the security checks are followed in sequence in the given time schedule and
the necessary action is taken. At the first level the user level agent gets activated and authenticates
the user. At the second level the process level agent gets activated and the user can use the
process allocated. At the next level the packet level agent gets activated and the user is allowed to
transmit files. Once all the necessary operations of the user is fulfilled the client panel sucessfully
terminates.
At the first level user is checked for authentication and if he is not authenticated he is treated as
an intruder. Next he is checked for the use of processes and if he is violating the allocated process
uasage he is treated as a process level intruder. At the third level if the file transmissions are
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crossing the fixed bytes of data he is treated as a packet level intruder.Adding to al these even
when the user tries to access at a time which is not scheduled he will be treated as an intuder [19].
Figure 8. Intrusion detected at Process Level.
Figure 9. Intrusion detected at Packet Level.
The results represent the intrusions detected at various levels of the security checks. For an
invalid user or intruder the security checks are explicit. All the events of intrusions are alerted to
the sytem administrator to his mobile phone to ensure that the intuder is blocked at the level at
which he is detected ensuring security to the IDS.
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Figure 10. Intrusion detection at Untime Login of user.
From the above results it can be concluded that our proposed system is capable of detecting
intrusions at various layers by using layered conditional random fields and when detected they
will be first intimated to the system administrator at the server side so that necessary actions can
be taken. The particular intruder will be denied of access thereby indicating that the intruder is
blocked at a particular level.
5. CONCLUSIONS
As security incidents become more numerous, IDS tools are becoming increasingly necessary.
They round out the security factor, working in conjunction with other information security tools,
such as firewalls, and allow for the complete supervision of all network activity. In our project we
have implemented a system for building robust and efficient intrusion detection systems by
implementing the layered conditional random fields using mobile phones.
Ideally, the best IDS tools combine both approaches. That way, the user gets comprehensive
coverage, making sure to guard against as many threats as possible. It is clear that using intrusion
detection systems is an important and necessary tool in the security manager's arsenal.
Our system addresses the problem of finding intruders effectively and blocking them as soon as
they are detected. The Layered Approach is a signature based system and the Conditional
Random Fields is an anomaly based system thus combining these both systems would result in a
hybrid system. Taking a thread from the integrated approach we have established scheduled user
login and successful communication with the system administrator through the mobile phones.
Our system can help in identifying an attack once it is detected at a particular layer, which
expedites the intrusion mechanism, thus minimizing the impact of an attack. Once the attack is
detected, it is intimated through mobile phone to the system administrator for safe guarding the
server system. This type of a system is very much suited in an organizational network. Finally,
13. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
191
our system has the advantage that the number of layers can be increased or decreased depending
upon the environment in which the system is deployed, giving flexibility to the network
administrator.
ACKNOWLEDGEMENT
The authors sincerely thank the authorities of Supercomputer Education and Research Center,
Indian Institute of Science for the encouragement and support.
REFERENCES
[1] Intrusion Detection Systems Basics. http://csrc.nist.gov/publications/nistpubs/800-94/SP800-94.pdf
[2] PengNing and SushilJajodia,(2003) “Intrusion Detection Techniques”, in H. Bidgoli (Ed.), The
Internet Encyclopedia, John Wiley & Sons.
[3] Harley Kozushko, (2003) Intrusion Detection: Host-Based and Network-Based Intrusion Detection
Systems.
http://infohost.nmt.edu/~sfs/Students/HarleyKozushko/Papers/IntrusionDetectionPaper.pdf
[4] SANS Institute, (2012) Intrusion Detection FAQ.http://www.sans.org/resources/idfaq/
[5] E. Tombini, H. Debar, L. Me, and M. Ducasse, (2003) “A Serial Combination of Anomaly and
Misuse IDSes Applied to HTTP Traffic”, Proc. 20th Annual Computer Security Applications
Conference (ACSAC’04), pp. 428-437.
[6] Kapil Kumar Gupta, BaikunthNath, KotagiriRamamohanarao, (2010)“Conditional Random Fields
for IntrusionDetection”,Proc. IEEE dependable and secure computing.
[7] McHugh, John, (2001) "Intrusion and Intrusion Detection", Technical Report, CERT Coordination
Center,Software Engineering Institute, Carnegie Mellon University.
[8] J. P. Anderson, (2010) “Computer Security Threat Monitoring and Surveillance”,http://csrc.nist.gov/
publications/history/ande80.pdf
[9] Y.-S. Wu, B. Foo, Y. Mei, and S. Bagchi, (2003)“Collaborative Intrusion Detection System (CIDS):
AFramework for Accurate and Efficient IDS”, Proc. 19th Ann. Computer Security Applications
Conf. (ACSAC ’03), pp. 234-244.
[10] R. Agrawal, T. Imielinski, and A. Swami, (1993)“Mining Association Rules between Sets of Items
in Large Databases”, Proc. ACM SIGMOD, vol. 22, no. 2, pp. 207-216.
[11] K.K. Gupta, B. Nath, and R. Kotagiri, (2006)“Network Security Framework”, Int’l J. Computer
Science and Network Security, vol. 6, no. 7B,pp. 151-157.
[12] K.K.Gupta, (2009)“Robust and Efficient Intrusion Detection Systems”,
ww2.cs.mu.oz.au/~kgupta/files/phd-completion.pdf
[13] Kapil Kumar Gupta, BaikunthNath, RamamohanaraoKotagiri, (2010) “Layered Approach Using
Conditional Random Fields for Intrusion Detection”, Proc. IEEE dependable and secure computing.
[14] N.B. Amor, S. Benferhat, and Z. Elouedi, (2004)“Naive Bayes vs.Decision Trees in Intrusion
Detection Systems”, Proc. ACM Symp.Applied Computing (SAC ’04), pp. 420-424.
[15] T. Abraham, (2008)“IDDM: Intrusion Detection Using Data Mining Techniques”.
http://www.dsto.defence./gov.au/publications/2345/DSTO-GD-0286.pdf
[16] C. Sutton and A. McCallum, (2006) “An Introduction to Conditional Random Fields for
RelationalLearning”, Introduction to Statistical Relational Learning, Edited by LiseGetoor and Ben
Taskar,Published by The MIT Press.
[17] SANS Institute, (2001) “Understanding Intrusion Detection Systems”, SANS Institute.
http://www.sans.org/reading_room/whitepapers/detection/understanding-intrusiondetectionsystems
14. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
192
[18] Rebecca Bace, “An Introduction to Intrusion Detection and Assessment for System and Network
Security Management”, ICSA, Inc.
http://www.icsalabs.com/icsa/docs/html/communities/ids/whitepaper/Intrusion1.pdf
[19] Arpitha M, Geetha V, Gowranga K H and Bhakthavathsalam R, (2013) “Test Suite for Intrusion
Detection by Layered Conditional Random Fields Using Mobile Phones”,Lecture Notes in
Electrical Engineering 131, Springer Science,NY, pp 537-549.
http://www.springer.com/engineering/signals/book/978-1-4614-6153-1
AUTHORS
Arpitha M has obtained her B.E. degree from the Dept of Information Science and Engineering, Alpha
College of Engineering affiliated to Visvesvaraya Technological University. She has successfully
completed her final semester project at IISc. She has presented a paper at the NetCom2012 conference. Her
interests are Wireless Technology and Network Security.
Geetha V has obtained her B.E. degree from the Dept of Information Science and Engineering, Alpha
College of Engineering, Bangalore affiliated to Visvesvaraya Technological University. She has
successfully completed her final semester project at IISc. She has published a paper in the NCS-2012
conference. Her interests are Network Security & Mobile Communication.
Mr.Gowranga K H is currently working as a Scientific Assistant in Supercomputer Education and
Research Center, IISc, Bangalore. His research interests include Wireless Networks, Webmail Systems, and
Digital Communication.
Dr.Bhakthavathsalam R is presently working as a Senior Scientific Officer in SERC, IISc, Bangalore. His
areas of interests are Electromagnetics, Wireless Networks and Pervasive Computing and Communication.
He is a Member of ACM and CSI.