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Network Intrusion Detection Using Multi-Objective Ensemble Classifiers
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Author(s): Arif Jamal Malik (Foundation University Islamabad, Pakistan)and Muhammad Haneef (Foundation University Islamabad, Pakistan)
Copyright: 2016
Pages: 15
Source title:
Innovative Solutions for Access Control Management
Source Author(s)/Editor(s): Ahmad Kamran Malik (COMSATS Institute of Information Technology, Pakistan), Adeel Anjum (COMSATS Institute of Information Technology, Pakistan)and Basit Raza (COMSATS Institute of Information Technology, Pakistan)
DOI: 10.4018/978-1-5225-0448-1.ch009
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Abstract
During the past few years, Internet has become a public platform for communication and exchange of information online. The increase in network usage has increased the chance of network attacks. In order to detect the malicious activities and threats, several kinds of Intrusion Detection Systems (IDSs) have been designed over the past few years. The goal of IDS is to intelligently monitor events occurring in a computer system or a network and analyze them for any sign of violation of the security policy as well as retain the availability, integrity, and confidentiality of a network information system. An IDS may be categorized as anomaly detection system or misuse detection system. Anomaly detection systems usually apply statistical or Artificial Intelligence (AI) techniques to detect attacks; therefore, these systems have the ability to detect novel or unknown attacks. A misuse detection system uses signature-based detection; therefore, these systems are good at identifying already known attacks but cannot detect unknown attacks.
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