Nearest cluster-based intrusion detection through convolutional neural networks

The recent boom in deep learning has revealed that the application of deep neural networks is a valuable way to address network intrusion detection problems. This paper presents a novel deep learning methodology that uses convolutional neural networks (CNNs) to equip a computer network with an effective means to analyse traffic on the network for signs of malicious activity. The basic idea is to represent network flows as 2D images and use this imagery representation of the flows to train a 2D CNN architecture. The novelty consists in deriving an imagery representation of the network flows through performing a combination of the nearest neighbour search and the clustering process. The advantage is that the proposed data mapping method allows us to build imagery data that express potential data patterns arising at neighbouring flows. The proposed methodology leads to better predictive accuracy when compared to competitive intrusion detection architectures on three benchmark datasets. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a novel deep learning methodology using convolutional neural networks for network intrusion detection, where network flows are represented as 2D images to analyze potential data patterns and achieve better predictive accuracy compared to competitive architectures.