Fuzzy Particle Swarm Optimization for Intrusion Detection

Boughaci, Dalila; Kadi, Mohamed Djamel Eddine; Kada, Meriem

doi:10.1007/978-3-642-34500-5_64

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…The values were obtained under different types of datasets such as KDD, NSL‐KDD, and Reliability Lab Data 2009 datasets . Except for NB , the rest of data mining and machine learning‐based methods show good detection accuracy. Many hybrid classifiers succeed at achieving a high detection rate while keeping a low false positive.…”

Section: Resultsmentioning

confidence: 99%

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Multivariate correlation analysis and geometric linear similarity for real‐time intrusion detection systems

Derhab

Bouras

2014

Security Comm Networks

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In this paper, we propose an intrusion detection system (IDS) based on four approaches: (i) statistical‐based IDS to reduce detection time; (ii) intertwining data acquisition phase and data preprocessing phase to ensure real‐time detection; (iii) geometric linear similarity measure that improves detection accuracy compared with existing measures; and (iv) multivariate correlation analysis that extracts a subset of strongly correlated features to construct a normal behavioral graph. Based on this graph, we derive the normal profile composed of high‐level features. We use NSL‐KDD dataset to analyze and evaluate the efficiency of the proposed IDS at detecting denial‐of‐service (DOS) attacks. Experimental results show that the proposed IDS can achieve good results in terms of detection rate and false positive rate. For some DOS attacks, 100% detection rate is achieved with 1.55% false positive. We also use KDD99 dataset to compare the proposed IDS with two statistical‐based methods and some data mining and machine learning‐based methods. Comparison study shows that the proposed IDS achieves the best tradeoff between detection rate (99.76%) and false positive rate (0.6%). It also requires just a few microseconds to classify the connection as normal or attack with low CPU usage and low memory consumption. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 99%

“…Some fuzzy techniques have also been used to tackle the intrusion issue. Boughaci et al , proposed a fuzzy PSO algorithm (FPSO), which combines between fuzzy logic and PSO. The same authors also proposed a fuzzy genetic algorithm (FGA) , which improves the fuzzy rules by adding a genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Multivariate correlation analysis and geometric linear similarity for real‐time intrusion detection systems

Derhab

Bouras

2014

Security Comm Networks

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“…Yi et al use a combination of statistical methods and particle swarm optimization (PSO) in order to achieve fast feature selection and rule optimization. 18 Dalila Boughaci et al propose a Fuzzy Particle Optimization (FPSO) algorithm for intrusion detection using "if-then" fuzzy rules as a knowledge base for modeling and improved by PSO algorithm 19 .…”

Section: Intelligent Optimization Algorithmsmentioning

confidence: 99%

GPR-RF: A Network Attack Traffic Detection Method Based on Random Forest and Bayesian Optimization

Du,

Lin,

Han

et al. 2023

Preprint

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Intrusion detection systems can identify intrusion processes which are attempting to intrude, in the process of intruding, or have already occurred.Intrusion detection is a proactive defense approach. Intrusion detection system models built by machine learning are very sensitive to hyper-parameter settings, and different combinations of hyper-parameters can dramatically affect the model's capabilities. In previous work, finding hyperparameters corresponding to advanced models is a difficult task. In order to deal with the huge amount of network traffic data, we here propose the Gaussian Process Regression and Random Forest (GPR-RF) method, which uses Bayesian optimization of Gaussian process regression to find more appropriate combinations of Random Forest hyperparameters, and realizes the following two advantages: 1. The accuracy of the model is greatly improved compared to the traditional methods. 2. The method can quickly converge to better configurations. Among several methods compared, our method performs best in both aspects.

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“…However, early and prompt detection of new and zero-day attacks is still a challenging area of research. Various machine learning techniques were applied for misuse-based detection [127,[391][392][393][394][395], anomaly-based detection [396][397][398][399] and hybrid-based detection [400][401][402][403][404]. Some papers summarized intrusion detection techniques and ML techniques in detail [3,73,77,[405][406][407][408][409][410][411][412][413].…”

Section: ) Trendsmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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Fuzzy Particle Swarm Optimization for Intrusion Detection

Cited by 9 publications

References 7 publications

Multivariate correlation analysis and geometric linear similarity for real‐time intrusion detection systems

Multivariate correlation analysis and geometric linear similarity for real‐time intrusion detection systems

GPR-RF: A Network Attack Traffic Detection Method Based on Random Forest and Bayesian Optimization

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

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