A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

Rabbani, Mahdi; Wang, Yongli; Khoshkangini, Reza; Jelodar, Hamed; Zhao, Ruxin; Bagheri Baba Ahmadi, Sajjad; Ayobi, Seyedvalyallah

doi:10.3390/e23050529

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A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies

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Sajjad Bagheri Baba Ahmadi

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Seyedvalyallah Ayobi

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Center for Applied Intelligent Systems Research (CAISR), Halmstad University, 30118 Halmstad, Sweden

Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada

Authors to whom correspondence should be addressed.

Academic Editors: Eirik Rosnes, Alexandre Graell i Amat and Hsuan-Yin Lin

Entropy 2021, 23(5), 529; https://doi.org/10.3390/e23050529

Received: 15 March 2021 / Revised: 11 April 2021 / Accepted: 20 April 2021 / Published: 25 April 2021

(This article belongs to the Special Issue Information-Theoretic Approach to Privacy and Security)

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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. View Full-Text

Keywords: machine learning; classifier systems; malicious behavior detection systems; dataset; data pre-processing

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Rabbani, M.; Wang, Y.; Khoshkangini, R.; Jelodar, H.; Zhao, R.; Bagheri Baba Ahmadi, S.; Ayobi, S. A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies. Entropy 2021, 23, 529. https://doi.org/10.3390/e23050529

AMA Style

Rabbani M, Wang Y, Khoshkangini R, Jelodar H, Zhao R, Bagheri Baba Ahmadi S, Ayobi S. A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies. Entropy. 2021; 23(5):529. https://doi.org/10.3390/e23050529

Chicago/Turabian Style

Rabbani, Mahdi, Yongli Wang, Reza Khoshkangini, Hamed Jelodar, Ruxin Zhao, Sajjad Bagheri Baba Ahmadi, and Seyedvalyallah Ayobi. 2021. "A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies" Entropy 23, no. 5: 529. https://doi.org/10.3390/e23050529

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