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.
|Number of pages||6|
|Publication status||Accepted/In press - 26 Mar 2023|
|Event||2023 IEEE Wireless Communications and Networking Conference (WCNC) - Scotland, Glasgow, United Kingdom|
Duration: 26 Mar 2020 → 29 Mar 2023
|Conference||2023 IEEE Wireless Communications and Networking Conference (WCNC)|
|Period||26/03/20 → 29/03/23|