This document proposes an intrusion detection framework that uses multiple binary classifiers optimized by a genetic algorithm. It analyzes decision trees, naive Bayes, and support vector machines to classify network connections as normal or attacks based on the NSL-KDD dataset. The classifiers are aggregated and a genetic algorithm is used to generate high-quality solutions. Experimental results show that the proposed method achieves 99% accuracy in intrusion detection, outperforming single classification techniques. The goal is to develop an application that can efficiently process network data and identify intrusion risks.
Intrusion detection system: classification, techniques and datasets to implement
This document discusses intrusion detection systems, including their classification techniques and datasets used to implement them. It first defines intrusion detection and intrusion detection systems, explaining that they monitor networks and systems for malicious activity and policy violations. It then categorizes intrusion detection systems as either network-based, host-based, or physical-based. The document also classifies intrusion detection approaches as either signature-based (misuse detection) or anomaly-based detection. It reviews several data mining techniques used for intrusion detection, including classification methods like decision trees, k-nearest neighbors, naive Bayes, and support vector machines. It also discusses clustering techniques. Finally, it mentions some commonly used intrusion detection datasets.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed Servers
This document discusses network security visualization and proposes a classification system for network security visualization systems. It begins by introducing the importance of visualizing network security data due to the large quantities of data produced. It then reviews existing network security visualization systems and outlines key aspects they monitor like host/server monitoring, port activity, and intrusion detection. The document proposes a taxonomy to classify network security visualization systems based on their data sources and techniques. It concludes by stating papers were selected for review based on their relevance to network security, novelty of techniques, and inclusion of evaluations.
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
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.
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
This document describes a proposed hybrid intrusion detection model that uses feature selection and machine learning algorithms with misuse detection. The model first selects important features from the NSL-KDD dataset and generates rules based on the behaviors of those features using J48 and CART algorithms. These rules are then used to build an intrusion detection framework that is tested on the NSL-KDD dataset, achieving an accuracy of 88.23%, outperforming other models that require prior learning of attacks. The proposed model works on the concept of misuse detection and can detect intrusions based on feature behaviors without any previous training.
Intrusion detection system: classification, techniques and datasets to implementIRJET Journal
This document discusses intrusion detection systems, including their classification techniques and datasets used to implement them. It first defines intrusion detection and intrusion detection systems, explaining that they monitor networks and systems for malicious activity and policy violations. It then categorizes intrusion detection systems as either network-based, host-based, or physical-based. The document also classifies intrusion detection approaches as either signature-based (misuse detection) or anomaly-based detection. It reviews several data mining techniques used for intrusion detection, including classification methods like decision trees, k-nearest neighbors, naive Bayes, and support vector machines. It also discusses clustering techniques. Finally, it mentions some commonly used intrusion detection datasets.
This document summarizes research on intrusion detection systems using data mining techniques. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using models, a data warehouse to store data and models, and a model generator to develop and distribute new models. It then discusses supervised and unsupervised learning approaches for intrusion detection. The document concludes by summarizing several papers on intrusion detection using techniques like neural networks, decision trees, clustering, and ensemble methods.
IRJET- 3 Juncture based Issuer Driven Pull Out System using Distributed ServersIRJET Journal
This document discusses network security visualization and proposes a classification system for network security visualization systems. It begins by introducing the importance of visualizing network security data due to the large quantities of data produced. It then reviews existing network security visualization systems and outlines key aspects they monitor like host/server monitoring, port activity, and intrusion detection. The document proposes a taxonomy to classify network security visualization systems based on their data sources and techniques. It concludes by stating papers were selected for review based on their relevance to network security, novelty of techniques, and inclusion of evaluations.
A Survey On Genetic Algorithm For Intrusion Detection SystemIJARIIE JOURNAL
The Internet has become a part of daily life and an essential tool today. Internet has been used as an important component of
business models. Therefore, It is very important to maintain a high level security to ensure safe and trusted communication of
information between various organizations.
Intrusion Detection Systems have become a needful component in terms of computer and network security. Intrusion detection is
one of the important security constraints for maintaining the integrity of information. Intrusion detection systems are the tools
used for prevention and detection of threats to computer systems. Various approaches have been applied in past that are less
effective to curb the menace of intrusion.
In this paper, a survey on applications of genetic algorithms in intrusion detection systems is carried out.
IRJET- Improving Cyber Security using Artificial IntelligenceIRJET Journal
This document discusses using artificial intelligence techniques like machine learning algorithms to improve cyber security. It proposes a methodology that uses Splunk to extract relevant fields from cybersecurity data, feeds that into a K-means clustering algorithm to form attack clusters, then sends those clusters to individual artificial neural networks (ANNs). The aggregated ANN results are then fed into a support vector machine (SVM) which classifies attacks as malicious, non-malicious, or benign. Testing this approach on a dataset achieved a classification accuracy of over 92% when using Splunk, K-means, ANNs, and SVM together.
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paper
This document proposes applying convolutional neural networks for network intrusion detection. It presents the methodology of using CNN and hybrid CNN-RNN models on two network intrusion detection datasets, KDDCup '99 and NSL-KDD. The results show CNN and hybrid models performed better than multi-layer perceptrons at classifying connections as normal or attacks and categorizing different attack types. Future work would evaluate these models on more recent intrusion detection datasets.
IRJET- A Review on Application of Data Mining Techniques for Intrusion De...IRJET Journal
This document reviews the application of data mining techniques for intrusion detection. It discusses different types of intrusion detection systems including misuse detection, anomaly detection, host-based IDS, network-based IDS, and hybrid IDS. It also covers the drawbacks of conventional IDS and various data mining algorithms that have been used for intrusion detection like decision trees, naive Bayes classifiers, k-means clustering, and SVM. The document concludes that integrating various data mining algorithms can help improve accuracy for attack prediction and classification in intrusion detection systems.
Automatic Insider Threat Detection in E-mail System using N-gram TechniqueIRJET Journal
This document summarizes research on automatic insider threat detection in email systems using n-gram techniques. It discusses how n-grams can be used to classify documents and detect potential data leakage through email. The system would verify emails sent outside the network using SHA, n-grams and thresholds. If a threat is detected, the user would be blocked. The document also provides a literature review on 10 other papers related to insider threat detection using methods like user profiling, activity logs, data mining and visualization techniques. It describes how n-grams work by breaking words into character sequences and creating profiles based on frequency to classify documents.
This document discusses and compares four main methodologies used in Intrusion Detection and Prevention Systems (IDPS): signature-based, anomaly-based, stateful protocol analysis-based, and hybrid-based. It provides details on how each methodology works and its strengths and weaknesses. A hybrid methodology that combines two or more of the other approaches is described as generally providing the best detection capabilities by taking advantage of the strengths of the individual methodologies. The document concludes by offering a way to evaluate IDPS systems based on parameters like resistance to evasion, accuracy, overhead, and other factors.
Finding Critical Link and Critical Node Vulnerability for Networkijircee
The document discusses network vulnerability assessment and finding critical links and nodes. It proposes using a belief propagation algorithm to calculate the vulnerability of each node and the overall network vulnerability over time. It provides an example network and shows the results of analyzing it to find the critical nodes and links using the proposed algorithm. The algorithm works by having each node calculate the vulnerability of its neighbors and share this information over time to determine the overall network vulnerability.
IRJET- Review on Intrusion Detection System using Recurrent Neural Network wi...IRJET Journal
This document summarizes research on using recurrent neural networks and deep learning for intrusion detection. It first provides background on intrusion detection systems and their types. It then discusses how deep learning approaches like recurrent neural networks can help address limitations of traditional machine learning methods for intrusion detection. The document reviews several related studies on intrusion detection using techniques like autoencoders, decision trees, and LSTM-RNNs. It proposes using a recurrent neural network model trained on the NSL-KDD dataset for intrusion detection, claiming this deep learning approach can improve classification accuracy compared to traditional machine learning methods.
IRJET- Review on Network Intrusion Detection using Recurrent Neural Network A...IRJET Journal
This document presents a review of using recurrent neural networks for network intrusion detection. It begins with an introduction to intrusion detection systems and the types of attacks they aim to detect. It then discusses previous research on machine learning approaches for intrusion detection, including the use of autoencoders, support vector machines, and other classifiers. The proposed approach uses a recurrent neural network for feature selection and classification of network data. The framework involves data collection, preprocessing including feature selection, training the recurrent neural network classifier, and then using the trained model to detect attacks in new data. Experimental results on benchmark intrusion detection datasets are presented and compared to other machine learning methods.
The document describes a hybrid honeypot framework for collecting and analyzing malware. The framework uses both client honeypots and server honeypots controlled by a central honeypot controller. Client honeypots actively visit URLs to detect client-side attacks, while server honeypots passively detect server-side attacks. Collected malware is stored in a central database and analyzed on an analysis server to detect known and unknown malware types through dynamic execution and static analysis. The integrated framework was able to collect thousands of malware samples, including some not detected by antivirus software.
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.
This document describes a distributed intrusion detection system based on honeypots. It proposes using honeypots to collect invasion characteristics on the network and genetic clustering algorithms to extract data for analysis. The system combines protocol analysis and signature detection modules to improve detection performance. An evaluation using KDDCUP 99 intrusion data showed the system can better detect intrusions and improve network security compared to traditional intrusion detection systems.
A PHASED APPROACH TO INTRUSION DETECTION IN NETWORKIRJET Journal
The document proposes a multi-stage approach to intrusion detection in networks. The approach consists of multiple stages: 1) pre-processing and feature extraction of network traffic data, 2) applying machine learning algorithms to build models that recognize normal behavior and identify potential intrusions, 3) using a rule-based system to further improve accuracy by classifying network activity, and 4) leveraging anomaly detection techniques to identify novel attacks. The goal is to provide a more accurate and efficient intrusion detection system to enhance network security.
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.
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 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.
MACHINE LEARNING AND DEEP LEARNING MODEL-BASED DETECTION OF IOT BOTNET ATTACKS.IRJET Journal
This document discusses machine learning and deep learning models for detecting IoT botnet attacks. It begins with an abstract that outlines the challenges of securing the growing number of IoT devices and describes how machine learning and deep learning techniques like LSTM RNN can be used to develop effective detection systems. The introduction provides background on botnets, distributed denial of service attacks, and the need for detection systems. The literature review then summarizes several previous works that used techniques such as Bayesian classifiers, random neural networks, decision trees, and other machine learning algorithms for attack detection. The methodology section outlines the general approach of anomaly-based intrusion detection systems and different learning methods. The experimental setup describes collecting and preprocessing data, feature extraction, model training and evaluation
FORTIFICATION OF HYBRID INTRUSION DETECTION SYSTEM USING VARIANTS OF NEURAL ...IJNSA Journal
Intrusion Detection Systems (IDS) form a key part of system defence, where it identifies abnormal
activities happening in a computer system. In recent years different soft computing based techniques have
been proposed for the development of IDS. On the other hand, intrusion detection is not yet a perfect
technology. This has provided an opportunity for data mining to make quite a lot of important
contributions in the field of intrusion detection. In this paper we have proposed a new hybrid technique
by utilizing data mining techniques such as fuzzy C means clustering, Fuzzy neural network / Neurofuzzy and radial basis function(RBF) SVM for fortification of the intrusion detection system. The
proposed technique has five major steps in which, first step is to perform the relevance analysis, and then
input data is clustered using Fuzzy C-means clustering. After that, neuro-fuzzy is trained, such that each
of the data point is trained with the corresponding neuro-fuzzy classifier associated with the cluster.
Subsequently, a vector for SVM classification is formed and in the last step, classification using RBF-
SVM is performed to detect intrusion has happened or not. Data set used is the KDD cup 1999 dataset
and we have used precision, recall, F-measure and accuracy as the evaluation metrics parameters. Our
technique could achieve better accuracy for all types of intrusions. The results of proposed technique are
compared with the other existing techniques. These comparisons proved the effectiveness of our
technique.
Review of Intrusion and Anomaly Detection Techniques IJMER
Intrusion detection is the act of detecting actions that attempt to compromise the
confidentiality, integrity or availability of a resource. With the tremendous growth of network-based
services and sensitive information on networks, network security is getting more and more importance
than ever. Intrusion poses a serious security threat in a huge network environment. The increasing use of
internet has dramatically added to the growing number of threats that inhabit within it. Intrusion
detection does not, in general, include prevention of intrusions. Now a days Network intrusion detection
systems have become a standard component in the area of security infrastructure. This review paper tries
to discusses various techniques which are already being used for intrusion detection.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
1.[1 9]a genetic algorithm based elucidation for improving intrusion detectio...Alexander Decker
This document summarizes a research paper that proposes using a genetic algorithm to improve intrusion detection by reducing features from the KDD 99 dataset. The genetic algorithm derives rules from the reduced training data and uses a fitness function to evaluate rule quality. The paper reviews related work applying genetic algorithms to intrusion detection. It describes the KDD 99 dataset used for training and testing. The proposed system incorporates a genetic algorithm and reduced feature set to detect and classify intrusions from normal network traffic.
Hyperparameters optimization XGBoost for network intrusion detection using CS...IAESIJAI
With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learning algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CICIDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
Application of Data Mining Technique in Invasion RecognitionIOSR Journals
This document discusses applying data mining techniques to network intrusion detection. It begins by introducing traditional intrusion detection methods and their limitations. It then establishes a data mining-based model for intrusion detection that is designed to address these limitations. The model collects network data, preprocesses it, applies data mining algorithms to extract patterns, and uses the patterns to detect intrusions. This allows detection of both known and unknown intrusion types while reducing false alarms and missed detections compared to traditional methods. Key aspects of the proposed system include data acquisition, preprocessing, pattern extraction using various data mining algorithms, and intrusion detection based on the extracted patterns.
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 System Using Machine Learning: An OverviewIRJET Journal
This document provides an overview of machine learning approaches for intrusion detection systems (IDS). It discusses how IDS use data mining techniques like classification, clustering, and association rule mining to detect network intrusions based on patterns in data. The document reviews several papers applying methods like ant colony optimization, support vector machines, genetic algorithms, and convolutional neural networks to classify network activities as normal or intrusive. It compares the strengths and limitations of different machine learning algorithms for IDS and identifies areas for potential improvement in future research.
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.
IRJET- Intrusion Detection System using Genetic AlgorithmIRJET Journal
This document discusses using a genetic algorithm to improve intrusion detection systems. It begins with an overview of intrusion detection systems and their classification as network-based or host-based. It then discusses genetic algorithms and how they can be applied to intrusion detection. Specifically, genetic algorithms can be used to generate rules for detecting normal and anomalous network connections in order to reduce false positives. The document concludes that implementing genetic algorithms in intrusion detection systems can help generate new rules dynamically and reduce the need for administrators to manually track and update rules.
Synthesis of Polyurethane Solution (Castor oil based polyol for polyurethane)IJARIIE JOURNAL
Around 160 million hector unused is available in India. India is the world’s largest producer of castor oil,
producing over 75% of the total world’s supply. There are over a hundred companies in India-small and
medium-that are into castor oil production, producing a variety of the basic grades o castor oil. All the above
factors make it imperative that the India industry relooks at the castor oil sector in order to devise suitable
strategies to derive the most benefits from such an attractive confluence of factors. Castor oil is unique owing to
its exceptional diversity of application. The oil and its derivatives are used in over 100 different applications in
diverse industries such as paints, lubricants, pharma, cosmetics, paper, rubber and more. Recent developments
have successfully derived polyol from natural oils and synthesized range of PU product from them. However,
making flexible solution from natural oil polyol is still proving challenging. The goal of this thesis is to
understand the potentials and the limitations of natural oil as an alternative to petroleum polyol. An initial
attempt to understand natural oil polyol showed that flexible solution could be synthesized from castor oil,
which produced a rigid solution. Characterization results indicate that the glass transition temperature (Tg) was
the predominant factor that determines the rigidity of the solution. The high Tg of solution was attributed to the
low number of covalent bond between cross linkers.
This document summarizes a research paper that proposes using an ensemble of k-nearest neighbor (k-NN) classifiers with genetic programming to improve network intrusion detection. The researchers trained classifiers on the KDD Cup 1999 dataset, which contains network traffic labeled as normal or an attack of various types. They preprocessed the data to remove redundancy and applied feature selection before training. The ensemble of k-NN classifiers classified data into five categories - one normal and four attack types - and achieved 99.97% accuracy on testing after genetic programming optimized the ensemble.
This document describes a proposed artificial neural network based intrusion detection system. It uses a multilayer perceptron neural network architecture trained on the KDD Cup 99 intrusion detection dataset. The system monitors network traffic in real-time, extracts features from network packets, and classifies the traffic into six categories using the neural network. It is able to detect both known and unknown attacks. The system aims to improve upon traditional signature-based intrusion detection systems.
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.
IRJET- Machine Learning based Network SecurityIRJET Journal
The document discusses using machine learning algorithms to classify network traffic as malicious or non-malicious. It describes capturing packets from a dummy website under distributed denial of service (DDoS) attack to create a dataset. Two machine learning algorithms, naive Bayes and support vector machines (SVM), are used to classify the network traffic. Both algorithms achieved over 98% accuracy in detecting spam traffic. The paper proposes creating a real-time network traffic classification system using machine learning algorithms to improve network security.
Similar to IRJET- An Intrusion Detection Framework based on Binary Classifiers Optimized by Genetic Algorithm (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Best Practices of Clothing Businesses in Talavera, Nueva Ecija, A Foundation ...IJAEMSJORNAL
This study primarily aimed to determine the best practices of clothing businesses to use it as a foundation of strategic business advancements. Moreover, the frequency with which the business's best practices are tracked, which best practices are the most targeted of the apparel firms to be retained, and how does best practices can be used as strategic business advancement. The respondents of the study is the owners of clothing businesses in Talavera, Nueva Ecija. Data were collected and analyzed using a quantitative approach and utilizing a descriptive research design. Unveiling best practices of clothing businesses as a foundation for strategic business advancement through statistical analysis: frequency and percentage, and weighted means analyzing the data in terms of identifying the most to the least important performance indicators of the businesses among all of the variables. Based on the survey conducted on clothing businesses in Talavera, Nueva Ecija, several best practices emerge across different areas of business operations. These practices are categorized into three main sections, section one being the Business Profile and Legal Requirements, followed by the tracking of indicators in terms of Product, Place, Promotion, and Price, and Key Performance Indicators (KPIs) covering finance, marketing, production, technical, and distribution aspects. The research study delved into identifying the core best practices of clothing businesses, serving as a strategic guide for their advancement. Through meticulous analysis, several key findings emerged. Firstly, prioritizing product factors, such as maintaining optimal stock levels and maximizing customer satisfaction, was deemed essential for driving sales and fostering loyalty. Additionally, selecting the right store location was crucial for visibility and accessibility, directly impacting footfall and sales. Vigilance towards competitors and demographic shifts was highlighted as essential for maintaining relevance. Understanding the relationship between marketing spend and customer acquisition proved pivotal for optimizing budgets and achieving a higher ROI. Strategic analysis of profit margins across clothing items emerged as crucial for maximizing profitability and revenue. Creating a positive customer experience, investing in employee training, and implementing effective inventory management practices were also identified as critical success factors. In essence, these findings underscored the holistic approach needed for sustainable growth in the clothing business, emphasizing the importance of product management, marketing strategies, customer experience, and operational efficiency.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
A vernier caliper is a precision instrument used to measure dimensions with high accuracy. It can measure internal and external dimensions, as well as depths.
Here is a detailed description of its parts and how to use it.
Encontro anual da comunidade Splunk, onde discutimos todas as novidades apresentadas na conferência anual da Spunk, a .conf24 realizada em junho deste ano em Las Vegas.
Neste vídeo, trago os pontos chave do encontro, como:
- AI Assistant para uso junto com a SPL
- SPL2 para uso em Data Pipelines
- Ingest Processor
- Enterprise Security 8.0 (Maior atualização deste seu release)
- Federated Analytics
- Integração com Cisco XDR e Cisto Talos
- E muito mais.
Deixo ainda, alguns links com relatórios e conteúdo interessantes que podem ajudar no esclarecimento dos produtos e funções.
https://www.splunk.com/en_us/campaigns/the-hidden-costs-of-downtime.html
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-leading-observability-practice.pdf
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-modern-security-program.pdf
Nosso grupo oficial da Splunk:
https://usergroups.splunk.com/sao-paulo-splunk-user-group/
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
Profiling of Cafe Business in Talavera, Nueva Ecija: A Basis for Development ...IJAEMSJORNAL
This study aimed to profile the coffee shops in Talavera, Nueva Ecija, to develop a standardized checklist for aspiring entrepreneurs. The researchers surveyed 10 coffee shop owners in the municipality of Talavera. Through surveys, the researchers delved into the Owner's Demographic, Business details, Financial Requirements, and other requirements needed to consider starting up a coffee shop. Furthermore, through accurate analysis, the data obtained from the coffee shop owners are arranged to derive key insights. By analyzing this data, the study identifies best practices associated with start-up coffee shops’ profitability in Talavera. These findings were translated into a standardized checklist outlining essential procedures including the lists of equipment needed, financial requirements, and the Traditional and Social Media Marketing techniques. This standardized checklist served as a valuable tool for aspiring and existing coffee shop owners in Talavera, streamlining operations, ensuring consistency, and contributing to business success.
Exploring Deep Learning Models for Image Recognition: A Comparative Reviewsipij
Image recognition, which comes under Artificial Intelligence (AI) is a critical aspect of computer vision,
enabling computers or other computing devices to identify and categorize objects within images. Among
numerous fields of life, food processing is an important area, in which image processing plays a vital role,
both for producers and consumers. This study focuses on the binary classification of strawberries, where
images are sorted into one of two categories. We Utilized a dataset of strawberry images for this study; we
aim to determine the effectiveness of different models in identifying whether an image contains
strawberries. This research has practical applications in fields such as agriculture and quality control. We
compared various popular deep learning models, including MobileNetV2, Convolutional Neural Networks
(CNN), and DenseNet121, for binary classification of strawberry images. The accuracy achieved by
MobileNetV2 is 96.7%, CNN is 99.8%, and DenseNet121 is 93.6%. Through rigorous testing and analysis,
our results demonstrate that CNN outperforms the other models in this task. In the future, the deep
learning models can be evaluated on a richer and larger number of images (datasets) for better/improved
results.
How to Manage Internal Notes in Odoo 17 POSCeline George
In this slide, we'll explore how to leverage internal notes within Odoo 17 POS to enhance communication and streamline operations. Internal notes provide a platform for staff to exchange crucial information regarding orders, customers, or specific tasks, all while remaining invisible to the customer. This fosters improved collaboration and ensures everyone on the team is on the same page.