Efficient Malicious Traffic Classification Methods based on Semi-supervised Learning

2022 9th International Conference on Dependable Systems and Their Applications (DSA)

Abstract

The proliferation of mobile communication systems, arrival of high-speed broadband networks and more complex network topologies have exacerbated cyber-threats. Cyber-warfare has become an aspect of modern war-fare that can no longer be overlooked. In recent years, network intrusions launched using the Internet have seriously undermined the security systems of many nations. Classifying malicious network traffic is the first step in network intrusion detection. In this paper, we propose three models using semi-supervised learning-based malicious traffic classification (MTC) methods that effectively improve the classification of traffic using a small proportion of labeled traffic data. Employing three different deep neural networks as feature extraction networks respectively, the proposed models use transductive transfer learning and domain adaptive ideas, and ladder networks as classification layers. Experimental results are provided to validate the proposed methods.

Authors 5

1. Xiaoyi Hu

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT,Nanjing,China

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

2. Jie Yin

   Jiangsu Police Officer College

   Affiliation as printed

   Jiangsu Police Institute,Department of Computer Information and Cyber Security,Nanjing,China

   Department of Computer Information and Cyber Security, Jiangsu Police Institute, Nanjing, China

3. Jie Yang

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   College of Telecommunications and Information Engineering, NJUPT,Nanjing,China

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

4. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University,Faculty of Science and Engineering,Manchester,United Kingdom

   Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom

5. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University,Aachen,Germany

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany