A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies |
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Authors: | Mahdi Rabbani Yongli Wang Reza Khoshkangini Hamed Jelodar Ruxin Zhao Sajjad Bagheri Baba Ahmadi Seyedvalyallah Ayobi |
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Institution: | 1.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; (R.Z.); (S.B.B.A.); (S.A.);2.Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden;3.Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada; |
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Abstract: | Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified. |
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Keywords: | machine learning classifier systems malicious behavior detection systems dataset data pre-processing |
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