Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

Tsang, Chi-Ho; Kwong, S.; Wang, Hanli

doi:10.1016/j.patcog.2006.12.009

Cited by 199 publications

(90 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…By using the covert channel, the attacker is able to install files from the victim machine and run UNIX commands remotely [26]. The total number of U2R attacks is 228 in [2], [7], [8], [12], [39]- [43], where the http-tunnel attack is considered as a U2R attack. In this study, the http-tunnel attack is taken as a R2L attack, and the number of U2R attacks is 70.…”

Section: Hybrid Model and Experimental Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Akyol

Hacıbeyoğlu

Karlık

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWith the increase of network components connected to the Internet, the need to ensure secure connectivity is becoming increasingly vital. Intrusion Detection Systems (IDSs) are one of the common security components that identify security violations. This paper proposes a novel multilevel hybrid classifier that uses different feature sets on each classifier. It presents the Discernibility Function based Feature Selection method and two classifiers involving multilayer perceptron (MLP) and decision tree (C4.5). Experiments are conducted on the KDD'99 Cup and ISCX datasets, and the proposal demonstrates better performance than individual classifiers and other proposed hybrid classifiers. The proposed method provides significant improvement in the detection rates of attack classes and Cost Per Example (CPE) which was the primary evaluation method in the KDD'99

show abstract

Section: Hybrid Model and Experimental Resultsmentioning

confidence: 99%

“…As performance metrics, The Detection Rates (DRs), Accuracy, and False Alarm Rate (FAR), which are commonly used in IDS related papers [2], [7], [8], [37], are calculated. (14), (15), and (16) describe the DR, FAR, and Accuracy, respectively.…”

Section: Evaluation Criteriamentioning

confidence: 99%

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Akyol

Hacıbeyoğlu

Karlık

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Tsang et al [73] proposed a fuzzy rule-based system for intrusion detection, which is evolved from an agent-based evolutionary framework and multiobjective optimization. The proposed system can also act as a genetic feature selection wrapper to search for an optimal feature subset for dimensionality reduction.…”

Section: Recent Applications Of Evolutionary Fuzzy Systems In Practicementioning

confidence: 99%

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

Abraham

Groşan

2008

Engineering Evolutionary Intelligent Systems

View full text Add to dashboard Cite

Summary. Designing intelligent paradigms using evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has evolved during the last couple of decades. We also provide a review of some of the recent interesting evolutionary intelligent system frameworks reported in the literature.

show abstract

“…The rules and patterns are useful to identifying intrusions in test data. Data mining [9] techniques such as decision trees [13] , genetic fuzzy rules [11] , neural networks [12] , support vector machine, principal component analysis [10] , naïve Bayesian classifiers and many other feature reduction [14] algorithms have been used widely to determine the network logs and to catch intrusion related information to get better correctness of IDS. The signature based IDS detects attacks on the known attack signature type.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System using Classification Technique

Wankhede¹,

Chole²

2016

IJCA

View full text Add to dashboard Cite

In today's world people are extensively using internet and thus are also vulnerable to its flaws. Cyber security is the main area where these flaws are exploited. Intrusion is one way to exploit the internet for search of valuable information that may cause devastating damage, which can be personal or on a large scale. Thus Intrusion detection systems are placed for timely detection of such intrusion and alert the user about the same. Intrusion Detection using hybrid classification technique consist of a hybrid model i.e. misuse detection model (AdTree based) and Anomaly model (svm based).NSL-KDD intrusion detection dataset plays a vital role in calibrating intrusion detection system and is extensively used by the researchers working in the field of intrusion detection. This paper presents Association rule mining technique for IDS. General TermsIntrusion Detection System, Preprocessing, Security et. al.

show abstract

Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

Cited by 199 publications

References 24 publications

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Design of Multilevel Hybrid Classifier with Variant Feature Sets for Intrusion Detection System

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

Intrusion Detection System using Classification Technique

Contact Info

Product

Resources

About