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

TY - CHAP

T1 - New Algorithms for the Detection of Malicious Traffic in 5G-MEC

AU - Fernando, Omesh A.

AU - Xiao, Hannan

AU - Spring, Joseph

PY - 2023/3

Y1 - 2023/3

N2 - 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.

AB - 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.

M3 - Conference paper

BT - 2023 IEEE Wireless Communications and Networking Conference (WCNC), 26–29 March 2023, Glasgow, Scotland, UK

PB - IEEE

ER -