Intrusion detection by machine learning: A review |
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| Authors: | Chih-Fong Tsai Yu-Feng Hsu Chia-Ying Lin Wei-Yang Lin |
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| Affiliation: | 1. Department of Information Management, National Central University, Taiwan;2. Department of Information Management, National Sun Yat-Sen University, Taiwan;3. Department of Accounting and Information Technology, National Chung Cheng University, Taiwan;4. Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan;1. Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, Taiwan;2. Department of Information and Computer Engineering, Chung Yuan Christian University, Taiwan;3. Department of Information Management, National Central University, Taiwan;1. Faculty of Computer and Information Technology, Jordan University of Science and Technology, Irbid, Jordan;2. College of Technological Innovation, Zayed University, United Arab Emirates;1. Computer Science Department, State University of Londrina (UEL), Rodovia Celso Garcia Cid, S/N, 86057-970 Londrina, Brazil;2. School of Computer Science (FACOM), Federal University of Uberlândia (UFU), Uberlândia, Brazil;1. Institute for IT Convergence, KAIST, Guseong-dong, Yuseong-gu, Daejeon 305-701, South Korea;2. Future Research Creative Laboratory, ETRI 218 Gajeong-ro, Yuseong-gu, Daejeon 305-700, South Korea |
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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. |
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