This document discusses network intrusion detection systems (NIDS) and their ability to handle high-speed traffic. It introduces NIDS and their role in monitoring network traffic. The document presents an experiment that tests the open-source NIDS Snort under high-volume traffic. The experiment shows that Snort drops more packets as traffic speed and volume increases, demonstrating a weakness of NIDS in high-speed environments. It suggests using a parallel NIDS technique to help NIDS better handle high-speed network traffic and reduce packet dropping.
Report: Study and Implementation of Advance Intrusion Detection and Preventio...
This document discusses building an intrusion detection system that combines network-based and log-based detection. It proposes using the Security Onion distribution and its included tools like Snort, Sguil, Squert and OSSEC. It describes configuring Security Onion sensors to monitor network traffic and logs, storing alerts in databases, and using the management consoles to analyze alerts. The goal is to create a comprehensive security monitoring platform through centralized log management and correlation of network and host-based events.
This document discusses denial of service (DoS) attacks and defense techniques. It begins by defining DoS attacks and describing common types like SYN floods, teardrop attacks, and ICMP floods. It then discusses various defense techniques including intrusion detection systems, intrusion prevention systems, and packet filtering firewalls. It compares the advantages and disadvantages of these different techniques. In conclusion, the document reviews that various techniques can be used to detect and prevent DoS attacks, with no single best approach, and defense requires a layered approach using multiple techniques.
Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection A...
This document describes a proposed approach for anomaly detection in intrusion detection systems using outlier detection. It begins with background on intrusion detection systems and issues with existing approaches. It then presents the proposed two-stage approach using outlier detection: 1) Training with large normal datasets in a distributed storage environment, and 2) Testing intrusion datasets to compute an error value compared to the trained model. If the error value exceeds a threshold, the test data is flagged as anomalous. Experimental results on network packet datasets demonstrate the approach can effectively identify anomalies.
Enhanced method for intrusion detection over kdd cup 99 dataset
This document discusses an enhanced method for intrusion detection using the KDD Cup 99 dataset. It aims to improve the accuracy of the dataset by analyzing the contribution of different attack classes to metrics like true positive rate and precision. The study examines these evaluation metrics for an intrusion detection system to identify which attack classes most impact recall and precision. The goal is to help improve the quality of the KDD Cup 99 dataset to achieve higher accuracy with lower false positives.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...
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.
Survey on Host and Network Based Intrusion Detection System
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
Analytical survey of active intrusion detection techniques in mobile ad hoc n...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
NETWORK INTRUSION DETECTION AND NODE RECOVERY USING DYNAMIC PATH ROUTINGNishanth Gandhidoss
This document describes a project report submitted for the degree of Bachelor of Technology in Information Technology. The report focuses on network intrusion detection and node recovery using dynamic path routing. It was submitted by three students - Nishanth G., Sudharshan N., and Surya Krishnan R. - to Sri Venkateswara College of Engineering in partial fulfillment of their degree requirements. The document includes sections on acknowledgements, abstract, contents, introduction, literature survey, system design, network topology, network intrusion detection and prevention, node recovery, source anonymity, dynamic path routing, results and discussions, and conclusions. It aims to address privacy and security issues in networks through techniques like encryption, evidence collection, risk assessment
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
Report: Study and Implementation of Advance Intrusion Detection and Preventio...Deepak Mishra
This document discusses building an intrusion detection system that combines network-based and log-based detection. It proposes using the Security Onion distribution and its included tools like Snort, Sguil, Squert and OSSEC. It describes configuring Security Onion sensors to monitor network traffic and logs, storing alerts in databases, and using the management consoles to analyze alerts. The goal is to create a comprehensive security monitoring platform through centralized log management and correlation of network and host-based events.
Denial of Service Attack Defense TechniquesIRJET Journal
This document discusses denial of service (DoS) attacks and defense techniques. It begins by defining DoS attacks and describing common types like SYN floods, teardrop attacks, and ICMP floods. It then discusses various defense techniques including intrusion detection systems, intrusion prevention systems, and packet filtering firewalls. It compares the advantages and disadvantages of these different techniques. In conclusion, the document reviews that various techniques can be used to detect and prevent DoS attacks, with no single best approach, and defense requires a layered approach using multiple techniques.
Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection A...Drjabez
This document describes a proposed approach for anomaly detection in intrusion detection systems using outlier detection. It begins with background on intrusion detection systems and issues with existing approaches. It then presents the proposed two-stage approach using outlier detection: 1) Training with large normal datasets in a distributed storage environment, and 2) Testing intrusion datasets to compute an error value compared to the trained model. If the error value exceeds a threshold, the test data is flagged as anomalous. Experimental results on network packet datasets demonstrate the approach can effectively identify anomalies.
Enhanced method for intrusion detection over kdd cup 99 datasetijctet
This document discusses an enhanced method for intrusion detection using the KDD Cup 99 dataset. It aims to improve the accuracy of the dataset by analyzing the contribution of different attack classes to metrics like true positive rate and precision. The study examines these evaluation metrics for an intrusion detection system to identify which attack classes most impact recall and precision. The goal is to help improve the quality of the KDD Cup 99 dataset to achieve higher accuracy with lower false positives.
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.
Survey on Host and Network Based Intrusion Detection SystemEswar Publications
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
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.
The spread of information networks in communities and organizations have led to a daily huge volume of information exchange between different networks which, of course, has resulted in new threats to the national organizations. It can be said that information security has become today one of the most challenging areas. In other words, defects and disadvantages of computer network security address irreparable damage for enterprises. Therefore, identification of security threats and ways of dealing with them is essential. But the question raised in this regard is that what are the strategies and policies to deal with security threats that must be taken to ensure the security of computer networks? In this context, the present study intends to do a review of the literature by using earlier researches and library approach, to provide security solutions in the face of threats to their computer networks. The results of this research can lead to more understanding of security threats and ways to deal with them and help to implement a secure information platform.
Cyber Warfare is the current single greatest emerging threat to National Security. Network security has become an essential component of any computer network. As computer networks and systems become ever more fundamental to modern society, concerns about security has become increasingly important. There are a multitude of different applications open source and proprietary available for the protection +-system administrator, to decide on the most suitable format for their purpose requires knowledge of the available safety measures, their features and how they affect the quality of service, as well as the kind of data they will be allowing through un flagged. A majority of methods currently used to ensure the quality of a networks service are signature based. From this information, and details on the specifics of popular applications and their implementation methods, we have carried through the ideas, incorporating our own opinions, to formulate suggestions on how this could be done on a general level. The main objective was to design and develop an Intrusion Detection System. While the minor objectives were to; Design a port scanner to determine potential threats and mitigation techniques to withstand these attacks. Implement the system on a host and Run and test the designed IDS. In this project we set out to develop a Honey Pot IDS System. It would make it easy to listen on a range of ports and emulate a network protocol to track and identify any individuals trying to connect to your system. This IDS will use the following design approaches: Event correlation, Log analysis, Alerting, and policy enforcement. Intrusion Detection Systems (IDSs) attempt to identify unauthorized use, misuse, and abuse of computer systems. In response to the growth in the use and development of IDSs, we have developed a methodology for testing IDSs. The methodology consists of techniques from the field of software testing which we have adapted for the specific purpose of testing IDSs. In this paper, we identify a set of general IDS performance objectives which is the basis for the methodology. We present the details of the methodology, including strategies for test-case selection and specific testing procedures. We include quantitative results from testing experiments on the Network Security Monitor (NSM), an IDS developed at UC Davis. We present an overview of the software platform that we have used to create user-simulation scripts for testing experiments. The platform consists of the UNIX tool expect and enhancements that we have developed, including mechanisms for concurrent scripts and a record-and-replay feature. We also provide background information on intrusions and IDSs to motivate our work.
A Modular Approach To Intrusion Detection in Homogenous Wireless NetworkIOSR Journals
This document discusses a modular approach to intrusion detection in homogeneous wireless networks. It begins by introducing wireless networks and the need for intrusion detection systems (IDS) due to security vulnerabilities. It then discusses different types of IDS, including signature-based detection that identifies known attacks, and anomaly-based detection that identifies deviations from normal behavior but can result in high false positives. The document proposes a modular approach combining advantages of signature-based and anomaly-based detection for high detection rates and low false positives. Requirements for IDS in wireless networks are also outlined.
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.
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.
IRJET- A Review on Intrusion Detection SystemIRJET Journal
This document provides a review of intrusion detection systems (IDS). It discusses the purpose of IDS in monitoring networks to detect anomalous behavior and security exploits. The document outlines the basic components and architecture of IDS, including sensors to collect data, an analyzer to examine data for intrusions, a knowledgebase of activity logs and signatures, and a user interface. It also covers different types of attacks IDS aims to detect, such as denial-of-service, spoofing and probing attacks. Finally, the document summarizes the typical workflow of an IDS in collecting data, selecting relevant features for analysis, analyzing data for intrusions, and taking appropriate actions in response.
Network Based Intrusion Detection and Prevention Systems: Attack Classificati...researchinventy
Complex and common security attackshave become a common issue nowadays. Success rate of detecting these attacks through existing tools seems to be decreasing due to simple rule-bases Some attacks are too complex to identify for today’s firewall systems.This paper highlights various security attacks classification techniques pertaining to TCP/IP protocol stack, it also covers an existingintrusion detection techniques used for intrusion detection , and features of various open source and commercial Network Intrusion Detection and Prevention (IDPS) tools. Finally paper concludes with comparison and evaluation of an open source and commercial IDPS tools and techniques which are used to detect and prevent the security attacks.
Augment Method for Intrusion Detection around KDD Cup 99 DatasetIRJET Journal
This document discusses augmenting methods for intrusion detection using the KDD Cup 99 dataset. It aims to improve detection accuracy and reduce false positives. The key points are:
- It analyzes detection precision and true positive rate (recall) for different attack classes in the KDD Cup 99 dataset to help improve dataset accuracy.
- Experimental results show the contribution of each attack class to recall and precision, which can help optimize the dataset to achieve highest accuracy with lowest false positives.
- The goal is to enhance testing of detection models and improve data quality to advance offline intrusion detection capabilities.
This document discusses network risks and vulnerabilities. It begins by defining vulnerabilities as software flaws or misconfigurations that weaken security. It then examines various types of vulnerabilities like design flaws, viruses, impersonation, worms, port scanning, man-in-the-middle attacks, denial-of-service attacks. The document also covers network risk assessment methodology and impact analysis. It concludes with a brief mention of network risk mitigation as a way to reduce risks.
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 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.
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.
This document discusses securing healthcare networks against cyber attacks. It proposes using intrusion detection systems to continuously monitor networks, firewalls to ensure endpoint devices comply with security policies, and biometrics for identity-based network access control. This would help protect patient privacy by safeguarding electronic health records and enhancing the security of hospital networks. The growing adoption of electronic records and devices in healthcare has increased risks of attacks that could intercept patient data or take over entire hospital networks. Strong network security measures are needed to address these risks.
Intrusion Detection Systems (IDSs) have become widely recognized as powerful tools for identifying, deterring and deflecting malicious attacks over the network. Intrusion detection systems (IDSs) are designed and installed to aid in deterring or mitigating the damage that can be caused by hacking, or breaking into sensitive IT systems. . The attacks can come from outsider attackers on the Internet, authorized insiders who misuse the privileges that have been given them and unauthorized insiders who attempt to gain unauthorized privileges. IDSs cannot be used in isolation, but must be part of a larger framework of IT security measures. Essential to almost every intrusion detection system is the ability to search through packets and identify content that matches known attacks. Space and time efficient string matching algorithms are therefore important for identifying these packets at line rate. In this paper we examine string matching algorithm and their use for Intrusion Detection. Keywords: System Design, Network Algorithm
Detecting and Preventing Attacks Using Network Intrusion Detection SystemsCSCJournals
Intrusion detection is an important technology in business sector as well as an active area of research. It is an important tool for information security. A Network Intrusion Detection System is used to monitor networks for attacks or intrusions and report these intrusions to the administrator in order to take evasive action. Today computers are part of networked; distributed systems that may span multiple buildings sometimes located thousands of miles apart. The network of such a system is a pathway for communication between the computers in the distributed system. The network is also a pathway for intrusion. This system is designed to detect and combat some common attacks on network systems. It follows the signature based IDs methodology for ascertaining attacks. A signature based IDS will monitor packets on the network and compare them against a database of signatures or attributes from known malicious threats. It has been implemented in VC++. In this system the attack log displays the list of attacks to the administrator for evasive action. This system works as an alert device in the event of attacks directed towards an entire network.
Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds if any malicious operations take place. Tremendous growth and practice of internet raises concerns about how to protect and communicate the digital data in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. Many intrusion detection techniques, methods and algorithms assist to identify these attacks. This main objective of this paper is to provide a complete study about the description of intrusion detection, history, life cycle, types of intrusion detection methods, types of attacks, different tools and techniques, research needs, tasks and applications
Intrusion Detection System (IDS) is meant to be a software application which monitors the network or system activities and finds if any malicious operations take place. Tremendous growth and practice of internet raises concerns about how to protect and communicate the digital data in a safe manner. Nowadays, hackers use different types of attacks for getting the valuable information. Many intrusion detection techniques, methods and algorithms assist to identify these attacks. This main objective of this paper is to provide a complete study about the description of intrusion detection, history, life cycle, types of intrusion detection methods, types of attacks, different tools and techniques, research needs, tasks and applications
A Comprehensive Review On Intrusion Detection System And TechniquesKelly Taylor
This document discusses machine learning techniques for intrusion detection systems (IDS). It provides an overview of the research progress using machine learning to improve intrusion detection in networks. Machine learning and data mining techniques have been widely used to automatically detect network traffic anomalies. The goal is to summarize and compare research contributions of IDS using machine learning, define existing challenges, and discuss anticipated solutions. Commonly used machine learning techniques for IDS are reviewed along with some existing machine learning-based IDS proposed by researchers.
Network Forensics is scientifically proven technique to accumulate, perceive, identify, examine, associate, analyse and document digital evidence from multiple systems for the purpose of uncovering the fact of attacks and other problem incident as well as performing the action to recover from the attack. Many systems are proposed for designing the network forensic systems. In this paper we have prepared comparative analysis of various models based on different techniques.
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 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.
Intrusion Detection System using AI and Machine Learning AlgorithmIRJET Journal
This document discusses using artificial intelligence and machine learning algorithms to develop an intrusion detection system (IDS). It begins with an abstract that outlines using AI to act as a virtual analyst to concurrently monitor network traffic and defend against threats. It then provides background on IDS and the need for more effective automated threat detection. The document discusses classifying attacks, different types of IDS (host-based and network-based), and detection methods like signature-based and anomaly-based. It aims to develop an IDS using machine learning algorithms that can learn patterns to provide automatic intrusion detection without extensive manual maintenance.
Intrusion Detection & Prevention Systems (IDPS) are crucial for protecting computers and detecting threats in real time. As threats have grown in the 21st century, IDPS have also evolved, with different types providing various protection functions. Effective IDPS not only detect and prevent attacks, but also log events, create reports on recent attacks, and provide detailed information. Detection methods include signature-based detection by comparing traffic to known attacks, anomaly-based detection by identifying deviations from normal behavior, and policy-based detection by enforcing allowed functions.
Network security is a dynamic art, with dangers appearing as fast as black hats can exploit vulnerabilities. While there are basic “golden rules” which can make life difficult for the bad guys, it remains a challenge to keep networks secure. John Chambers, Executive Chairman of Cisco, famously said “there are two types of companies: those that have been hacked, and those who don’t know they have been hacked”. The question for most organizations isn’t if they’re going to be breached, but how quickly they can isolate and mitigate the threat. In this paper, we’ll examine best practices for effective cybersecurity – from both a proactive (access hardening) and reactive (threat isolation and mitigation) perspective. We’ll address how network automation can help minimize cyberattacks by closing vulnerability gaps and how it can improve incident response times in the event of a cyberthreat. Finally, we’ll lay a vision for continuous network security, to explore how machine-to-machine automation may deliver an auto-securing and self-healing network.
Go to www.esgjrconsultinginc.com
Toward Continuous Cybersecurity With Network AutomationKen Flott
Network security is a dynamic art, with dangers appearing as
fast as black hats can exploit vulnerabilities. While there are
basic “golden rules” which can make life difficult for the bad
guys, it remains a challenge to keep networks secure. John
Chambers, Executive Chairman of Cisco, famously said “there
are two types of companies: those that have been hacked, and
those who don’t know they have been hacked”. The question
for most organizations isn’t if they’re going to be breached, but
how quickly they can isolate and mitigate the threat.
In this paper, we’ll examine best practices for effective
cybersecurity – from both a proactive (access hardening)
and reactive (threat isolation and mitigation) perspective.
We’ll address how network automation can help minimize
cyberattacks by closing vulnerability gaps and how it can
improve incident response times in the event of a cyberthreat.
Finally, we’ll lay a vision for continuous network security, to
explore how machine-to-machine automation may deliver an
auto-securing and self-healing network.
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.
How to Handle the Separate Discount Account on Invoice in Odoo 17Celine George
In Odoo, separate discount account can be set up to accurately track and manage discounts applied on various transaction and ensure precise financial reporting and analysis
How to Store Data on the Odoo 17 WebsiteCeline George
Here we are going to discuss how to store data in Odoo 17 Website.
It includes defining a model with few fields in it. Add demo data into the model using data directory. Also using a controller, pass the values into the template while rendering it and display the values in the website.
How to Configure Time Off Types in Odoo 17Celine George
Now we can take look into how to configure time off types in odoo 17 through this slide. Time-off types are used to grant or request different types of leave. Only then the authorities will have a clear view or a clear understanding of what kind of leave the employee is taking.
How to Add Colour Kanban Records in Odoo 17 NotebookCeline George
In Odoo 17, you can enhance the visual appearance of your Kanban view by adding color-coded records using the Notebook feature. This allows you to categorize and distinguish between different types of records based on specific criteria. By adding colors, you can quickly identify and prioritize tasks or items, improving organization and efficiency within your workflow.
Front Desk Management in the Odoo 17 ERPCeline George
Front desk officers are responsible for taking care of guests and customers. Their work mainly involves interacting with customers and business partners, either in person or through phone calls.
Ardra Nakshatra (आर्द्रा): Understanding its Effects and RemediesAstro Pathshala
Ardra Nakshatra, the sixth Nakshatra in Vedic astrology, spans from 6°40' to 20° in the Gemini zodiac sign. Governed by Rahu, the north lunar node, Ardra translates to "the moist one" or "the star of sorrow." Symbolized by a teardrop, it represents the transformational power of storms, bringing both destruction and renewal.
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Webinar Innovative assessments for SOcial Emotional SkillsEduSkills OECD
Presentations by Adriano Linzarini and Daniel Catarino da Silva of the OECD Rethinking Assessment of Social and Emotional Skills project from the OECD webinar "Innovations in measuring social and emotional skills and what AI will bring next" on 5 July 2024
Principles of Roods Approach!!!!!!!.pptxibtesaam huma
Principles of Rood’s Approach
Treatment technique used in physiotherapy for neurological patients which aids them to recover and improve quality of life
Facilitatory techniques
Inhibitory techniques
How to Create Sequence Numbers in Odoo 17Celine George
Sequence numbers are mainly used to identify or differentiate each record in a module. Sequences are customizable and can be configured in a specific pattern such as suffix, prefix or a particular numbering scheme. This slide will show how to create sequence numbers in odoo 17.
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1. 2013 IEEE 10th International Conference
on e-Business Engineering
Network intrusion detection systems in high-speed traffic in
computer networks
Waleed Bul’ajoul Anne James
Mandeep Pannu
Faculty of Engineering and Faculty of
Engineering and Faculty of Engineering and
Computing Computing
Computing
Coventry University Coventry
University Coventry University
Coventry, UK Coventry, UK
Coventry, UK
Bulajouw@coventry.ac.uk
Csx118@coventry.ac.uk Aa3371@coventry.ac.uk
Abstract—With the various and increasingly malicious
inadvisable to depend only on prevention techniques,
attacks on networks and wireless systems, traditional
especially when an attacker has successfully obtained
security tools such as anti-virus programs and firewalls are
vulnerable information from a network, but prevention can
not sufficient to provide free, integrated, reliable and secure
successfully and effectively restore a network before an
networks. Intrusion detection systems (IDSs) are one of the
attack is launched.
most tested and reliable technologies to monitor incoming
Correction techniques are adopted to protect computer
and outgoing network traffic to identify unauthorized usage
systems. Along with prevention, they actively work to
and mishandling of computer system networks.
block intrusions, but can continue to battle a successful
It is critical to implement network intrusion detection
intrusion. Nevertheless, a number of successful attacks can
systems (NIDSs) in computer networks that have high traffic
be controlled using prevention techniques if an attack is
and high-speed connectivity. Due to the fact that software
detected at the interim stage of prevention systems. This is
NIDSs are still unable to detect all the growing threats to
difficult, because some successful attacks can get through
high-speed environments, such as flood attacks (UDP, TCP,
the prevention system [1]. It is a matter of a system being
ICMP and HTTP) or Denial and Distributed Denial of
attacked, compromised, and consequently malfunctioning.
Service Attacks (DoS/DDoS), because the main function of
Here we need an interim stage such as the detection phase,
these kinds of attacks is simply to send more traffic in high
which should be positive during intrusions. Therefore, the
2. speed to systems to stop or slow down the performance of
detection method is preferred to minimize network costs
systems.
and fill in the gap between correction and prevention
mechanisms.
Here we have designed a suitable real network to present
Intrusion Detection (ID) is one of the most tested and
experiments that use Snort NIDSs to demonstrate the
reliable technologies to monitor incoming and outgoing
weaknesses of NIDSs, such as its inability to process multiple
network traffic to identify unauthorized usage and
packets at high speeds and its propensity to drop packets
mishandling of computer system networks [3, 4]. In
without analysing them. This paper outlines Snort NIDSs’
addition, ID identifies the activity of malicious attackers.
failures in high-speed and heavy traffic and its propensity to
Due to the fact that numerous computer systems are
drop more packets as the speed and volume of traffic
unable to prevent threats such as flood attacks, DoS
increase. We ran some consecutive tests to analyse the Snort
performance using the number of packets received, the
attacks and DDoS attacks affect many systems, because
number of packets analysed, the number of packets filtered
the impact of such attacks is severe and irrevocable. The
and the number of packets dropped. We suggest a parallel
main function of these kinds of attacks is to send more
NIDS technology to reduce dropping packets as a solution.
high-speed traffic to a network address, which stops or
slows down the performance of legitimate users’ computer
Keywords-network security; open source; IDS.
network systems by exploiting vulnerabilities such as mis-
configurations and software bugs generated from internal
I. INTRODUCTION
and external networks.
Security is a major concern in every aspect of our daily
It is critical to implement ID systems (IDSs) in
life. New methods and equipment have been devised to
computer networks that have high traffic and high-speed
ensure privacy. However, computer networks still face
connectivity [5]. IDSs consist of either software
many threats [1]. There are usually three stages to
applications or hardware to listen for and detect malicious
achieving security in computer system networks:
activities at the gateways (incoming and outgoing) of
prevention, detection and correction [2]. Prevention is
individual or network systems. Therefore, an IDS’s
preferable to detection and correction, but it is impossible
sniffing mechanism is effectively applied at the network
4. In this paper, we present an experiment to test Snort
circumstances in the network, which results in an
NIDS under high-volume and heavy traffic, demonstrating
inaccurate detection mechanism. IDSs are still unable to
that it drops more packets as the speed and volume of
control all threats and malicious activities [1, 9]. To
traffic increase. We will also prove that the performance
overcome such design and implementation difficulties,
and capability of NIDS can be increased and the
novel IDS outcomes have been obtained from multiple
processing time of network traffic can decreased and
characteristics of advanced computer networks:
effectively handled by an alternative technique known as
Processing in real time;
parallel NIDS.
High speeds and high loads;
Reducing difficulties for defenders; and
II. BACKGROUND
Increasing difficulties for attackers.
The specialized IDS mechanism is based on how,
A. Security Products
where and what it detects, along with mandatory
Security products such as firewalls and antivirus
requirements. In particular, IDSs should be based on
programs are less efficient than IDSs and have different
flexible and scalable network components to accommodate
functionalities. IDSs analyse collected information and
the drastic increase in today’s network environments. They
infer more useful results than other security products. The
should provide straightforward management and
difference between IDSs and security products such as
operational procedures and steps rather than complicating
antivirus programs is that, while IDSs require more
underlying tasks, and they should provide user-friendly ID
intelligence than security product software, they analyse
mechanisms.
gathered information and deduce useful results [1].
1) Firewall technology
III. INTRUSION DETECTION SYSTEM
Network traffic is usually filtered according to criteria
such as origin, destination, protocol and service, typically
An intrusion detection system (IDS) is used to make
5. through dedicated routers called firewalls [1].
security professionals aware of packets entering and
The functionality of the firewall is based on filtering
leaving a monitored network. IDSs are often used to sniff
mechanisms specified by a set of rules, known as a policy,
out network packets, thereby providing a good
which can protect a system from flooding attacks [1]. The
understanding of what is really happening on the network.
basic operation of firewalls is to filter packets passing
An IDS is based on either hardware or software, where
through specific hosts or network ports, which are usually
incoming and outgoing individuals and/or network traffic
open in most computer systems [1]. It does not perform
have been listened to, and has the potential to detect and
deep analysis (malicious code detection in the packet) and
report any evidence of attacks [4, 10].
treats each packet as an individual entity [1].
The typical actions of IDS software can be classified as
The disadvantage of a firewall is that it cannot fully
follows:
protect an internal network; it is unable to stop internal
Monitoring entire and/or partial packets;
attacks [1, 6]. For example, malicious and unwanted web
Detecting suspicious activities;
traffic can go through a firewall to strike and damage a
Recording required events; and
protected computer system without a hitch.
Sending updates to the network administrator.
2) Anti-virus technology
IDS are classified into three main types: network-
Computer viruses are programs which cause computer
based, host-based and hybrid.
failure and damage computer data. Especially in a network
environment, a computer virus poses an immeasurable
A. Network-based IDSs
threat and can be very destructive [6]. The functionality of
Network-based IDSs (NIDSs) have become a critical
an anti-virus program is a running process that examines
component of an organization’s security solution [24]. An
executables, worms and viruses in the memory of guarded
NIDS is capable of detecting a broad range of malicious
computer/network systems instead of monitoring network
and unwanted attacks occurring in an application, network
traffic.
and transport layers, along with unexpected services based
Although an anti-virus program monitors the integrity
on multiple applications. In addition, NIDSs are able to
of data files against illegal modifications, it is unable to
detect and monitor network traffic and secure computer
block unwanted network traffic intended to damage the
systems from network-based threats without network
network. [7].
policy violations [11].
6. 3) IDS technology
Disadvantaged NIDS are usually unable to execute
Firewalls have been used for network security for a
entire network packets, which results in incomplete
long time, but they can be easily bypassed, as a lot of
analyses and therefore considerable delays in high-speed
techniques for deceiving firewalls have been developed
and high-load environments [11].
[8]. IDSs are much more advanced and enhanced security
tools than firewalls, because a firewall just drops
B. Host-based IDSs
packets—it cannot detect intrusion [1].
Host-based IDSs (HIDSs) are implemented to monitor
In addition, it is difficult to detect suspicious activities
suspected events happening in local host machines. HIDS
in the midst of high traffic and other such adverse
are versatile due to their installation over servers,
169
7. workstations and notebooks, as compared to NIDS. In
C. Protocol Anomaly detection (Models are built on
addition, HIDSs are capable of monitoring malicious
TCP/IP protocols using their specifications).
networks and multiple events happening within the
Intruders usually use signatures which behave similarly
protected host. An HIDS is situated at the end point of a
to viruses used in computers. Protocol anomaly detection
computer network that has anti-threat applications such as
analyses data packets related to IPs, which contain known
spyware detection, firewalls and antivirus software
anomalies and single or sets of signatures. The detection
programs, which provide access to outside environments
system is capable of detecting suspicious activity in the
such as the Internet [11].
logs and generates alterations based on these signatures
The disadvantages of an HIDS are as follows:
and rules. On the other hand, anomaly-based IDSs
It consumes computer system resources that
generally depend on detecting packet anomalies available
should be allocated for services.
in the header parts of the protocol.
It may conflict with existing security policies of
firewalls and operating systems.
V. SIGNATURE-BASED DETECTION
It cannot easily analyse intrusion attempts on
A signature is generally based on an observable pattern
multiple computers.
inside the data packet. This technique helps to detect
It can be very difficult to maintain in large
several kinds of attacks and the presence of intrusive
networks with different operating systems and
activity, such as the presence of “scripts/iisad-min” in a
configurations.
packet used for web services. However, it is difficult to
It can be disabled by attackers after the system is
sort out the signatures in the headers of IP, UPD, TCP,
compromised.
Application Layer and Payload [12].
It requires many hosts to reboot after a complete
Signature-based IDSs are efficient at detecting pre-
installation or an update [5]. Many essential
defined attacks, but they increase the size of databases
servers cannot support this operation.
because each available signature must be entered or made
available in the database. Therefore, each arriving packet
C. Hybrid-based IDS
can be compared with the signatures available in the
In some situations, HIDSs and NIDSs may unable to
database, but this reduces the efficiency of the system in
fulfil the requirements for intrusion detection because any
terms of time and throughput and increases network
8. one type of IDS has both inherent virtues and
delays.
shortcomings. Therefore, a combination of an HIDS and
It is now common to test NIDSs in high-speed
an NIDS is known as a Hybrid IDS [5].
infrastructures with large amounts of data, but they are
unable to rectify malicious activities and threats. NIDS are
IV. NETWORK INTRUSION DETECTION SYSTEM
effective and useful in controlling malicious activity and
METHODOLOGY
threats under circumstances where traffic is constantly
NIDS can be classified into three (3) fundamental
growing [7, 13]. NIDSs are further classified into
categories:
software- or hardware-based. It is also observed that
software-based NIDSs still require enhancement for a
A. Anomaly-based IDSs
network with a high volume of high-speed data, but they
Anomaly-based IDSs require a background of
are useful for small networks [13, 14]. However, one of
foundation-based information and need particular
the most strong and popular open-source NIDS is Snort.
knowledge of the system being protected. Such systems
The Snort NIDS was introduced as a light-weight-
have profound merit in gathering evidence in the form of
based IDS [13, 14], but due to the drastic growth of
statistics, data, facts, and figures, which are responsible for
technology, it has been significantly improved as well
the formation of baselines during the learning period.
[15]. It consists of a combination of language rule driving
The baseline profile is the normal learned behaviour of
and signatures, where protocol anomaly- and signature-
the monitored system and is developed during the learning
based inspection methods are used [15]. In spite of the
period, while the IDS learns the environment and develops
huge development over the years, Snort is still struggling
a normal profile of the monitored system. This
to sustain its growth in the network industry and attacks.
environment can be a network, users, a system, etc. [9].
Many studies indicate that it is unable to cope with recent
Anomaly-based IDSs are further classified into the
attacks [15, 16], because multiple threats and attacks such
following anomalies:
ICMP, HTTP, UDP (flood attacks) and DDoS attacks have
Protocol-based Anomaly
adopted high traffic and speed to attack a system.
Application Payload-based Anomaly
VI. SNORT OVERVIEW
B. Signature-based IDSs
Snort is accessible free of cost and is ranked among the
top systems available nowadays with the best features. It is
9. released as an open-source NIDS based on a rule-based
IDS, which stores information in text files; such text files
can be modified by a text editor. Rules are grouped into
170