Per Erik Strandberg /cv /kurser /blog

Network Intrusion Detection using Machine Learning on Resource-Constrained Edge Devices is a paper being published at the IEEE WCCI IJCNN conference in Yokohama, Japan, July 2024. Authors are Pontus Lidholm, Tijana Markovic, Miguel Leon, and me Per Erik Strandberg

Link to PDF will come soon enough.

Abstract: The rapid growth of the Internet has led to the evolution of sophisticated security threats that exploit vulnerabilities within networks. The defence mechanisms must quickly adapt to these new threats to ensure that networks stay secure. One possible mechanism is to use Machine Learning (ML) algorithms to detect malicious activities. The edge devices that control and manage the network, such as routers, already have access to the data that is flowing through the network and may utilize its own computational resources to host ML algorithms and use them to detect intrusions. This paper presents a system for network intrusion detection which is deployed to an edge device and evaluated for live binary classification of network traffic. Different ML algorithms (Decision Tree, Random Forest, and Artificial Neural Network) are evaluated on existing datasets (Westermo and CIC-IDS-2017). Flow-based data pre-processing is performed and different labeling strategies and flow durations are used and compared. The most effective version of each algorithm is implemented and deployed on the Westermo Lynx-3510 routing-capable network switch and system performance is assessed across various scenarios with simulated network attacks.
The experiments showed that Random Forest is the best option, closely followed by Decision Tree.

Keywords: Intrusion Detection, Machine Learning, Edge Computing, Embedded System

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