Review: Intrusion detection by machine learning: A review

Authors:
Chih-Fong Tsai;Yu-Feng Hsu;Chia-Ying Lin;Wei-Yang Lin
Affiliations:
Department of Information Management, National Central University, Taiwan;Department of Information Management, National Sun Yat-Sen University, Taiwan;Department of Accounting and Information Technology, National Chung Cheng University, Taiwan;Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 35
Cited 14

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Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

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On Combining Classifiers

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A framework for constructing features and models for intrusion detection systems

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Data Mining Methods for Detection of New Malicious Executables

SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Adaptive Neuro-Fuzzy Intrusion Detection Systems

ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Intrusion detection using an ensemble of intelligent paradigms

Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Application of SVM and ANN for intrusion detection

Computers and Operations Research
Intrusion detection using hierarchical neural networks

Pattern Recognition Letters
Using artificial anomalies to detect unknown and known network intrusions

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Identifying Intrusions in Computer Networks with Principal Component Analysis

ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
A clustering-based method for unsupervised intrusion detections

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Decision tree classifier for network intrusion detection with GA-based feature selection

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Neural Networks: A Comprehensive Foundation (3rd Edition)

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A latent class modeling approach to detect network intrusion

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Hybrid flexible neural-tree-based intrusion detection systems: Research Articles

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Letters: A hierarchical intrusion detection model based on the PCA neural networks

Neurocomputing
A hierarchical SOM-based intrusion detection system

Engineering Applications of Artificial Intelligence
Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening

Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Modeling intrusion detection system using hybrid intelligent systems

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

Pattern Recognition
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SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
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An empirical analysis of the probabilistic K-nearest neighbour classifier

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A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers

Computer Communications
A new intrusion detection system using support vector machines and hierarchical clustering

The VLDB Journal — The International Journal on Very Large Data Bases
Intrusion detection in computer networks by a modular ensemble of one-class classifiers

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A parallel genetic local search algorithm for intrusion detection in computer networks

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Computer Security: Principles and Practice

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Application of online-training SVMs for real-time intrusion detection with different considerations

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Intrusion detection using PCASOM neural networks

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Intrusion detection through learning behavior model

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Editorial: A representative bibliography of surveys in the information fusion domain

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ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
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Testing ensembles for intrusion detection: On the identification of mutated network scans

CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
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Nonparametric semi-supervised learning for network intrusion detection: combining performance improvements with realistic in-situ training

Proceedings of the 5th ACM workshop on Security and artificial intelligence
Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems

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New class-dependent feature transformation for intrusion detection systems

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Opcode sequences as representation of executables for data-mining-based unknown malware detection

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Survey An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique

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Abstract

The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.