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Future Internet 2017, 9(4), 81; doi:10.3390/fi9040081

Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression

1
Institute of Artificial Intelligence, College of Computer Science, Zhejiang University, Hangzhou 310027, China
2
State Key Laboratory of ICT, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
3
COMSATS Institute of Information Technology, Lahore 54500, Pakistan
4
Department of Computer System engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
*
Author to whom correspondence should be addressed.
Received: 18 October 2017 / Revised: 1 November 2017 / Accepted: 6 November 2017 / Published: 10 November 2017
(This article belongs to the Special Issue Information Systems Security)
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Abstract

Intrusion detection system (IDS) is a well-known and effective component of network security that provides transactions upon the network systems with security and safety. Most of earlier research has addressed difficulties such as overfitting, feature redundancy, high-dimensional features and a limited number of training samples but feature selection. We approach the problem of feature selection via sparse logistic regression (SPLR). In this paper, we propose a discriminative feature selection and intrusion classification based on SPLR for IDS. The SPLR is a recently developed technique for data analysis and processing via sparse regularized optimization that selects a small subset from the original feature variables to model the data for the purpose of classification. A linear SPLR model aims to select the discriminative features from the repository of datasets and learns the coefficients of the linear classifier. Compared with the feature selection approaches, like filter (ranking) and wrapper methods that separate the feature selection and classification problems, SPLR can combine feature selection and classification into a unified framework. The experiments in this correspondence demonstrate that the proposed method has better performance than most of the well-known techniques used for intrusion detection. View Full-Text
Keywords: sparse logistic regression (SPLR); intrusion detection system (IDS); computer network security; data mining (DM); machine learning (ML) sparse logistic regression (SPLR); intrusion detection system (IDS); computer network security; data mining (DM); machine learning (ML)
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MDPI and ACS Style

Shah, R.A.; Qian, Y.; Kumar, D.; Ali, M.; Alvi, M.B. Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression. Future Internet 2017, 9, 81.

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