New Algorithms for the Detection of Malicious Traffic in 5G-MEC

Omesh A. Fernando, Hannan Xiao, Joseph Spring

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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This paper presents a new Intrusion Detection System (IDS) using a 3 layer Convolutional Neural Network (CNN), capable of identifying malicious network traffic. We employ a new injective algorithm to encode network traffic without loss of information. We also include a new algorithm to decode, encoded RGB images back into network traffic. We evaluate the proposed IDS in terms of its computational complexity in for example: time, memory and CPU utilisation for the encoding and decoding algorithms, and its accuracy and loss during training and detection. Lastly, we compare the proposed IDS against a significant IDS algorithm that uses a different approach for encoding, decoding and CNN detection.
Original languageEnglish
Title of host publication2023 IEEE Wireless Communications and Networking Conference (WCNC), 26–29 March 2023, Glasgow, Scotland, UK
Number of pages6
Publication statusAccepted/In press - Mar 2023


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