Intrusion detection model using fusion of chi-square feature selection and multi class SVM

By Sumaiya Thaseen Ikram, Cherukuri Aswani Kumar. Journal of King Saud University: Computer and Information Sciences, 2016

448 citations recorded by the source corpus.

Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS) employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM). A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/d2d29c36cf4f48c30cc2dfc0434e3dc1fc3231bb

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Citations in the corpus

Network Intrusion Detection Combined Hybrid Sampling With Deep Hierarchical Network (2020, methodology): “Sumaiya proposed an intrusion detection model using chi-square feature selection and multi class support vector machine which could get a better detection rate and reduced false alarm rate [10].”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “Reduction Feature selection Automation Programmatic [10, 228] Feature selection Automation Learning-based [244, 257] Feature selection Collaboration Partial [208, 286] Dimensionality reduction Automation Learning-based [2, 13, 254] Instance selection Automation Programmatic [186, 193][148] Instance ”
A Review on Machine Learning and Deep Learning Perspectives of IDS for IoT: Recent Updates, Security Issues, and Challenges (2020, methodology): “SVM are used for bui-dling IDS for mobile ad-hoc networks in [81], where IDS detects intrusions and removes the malicious nodes from the network.”