Academic Papers

On the Use and Reuse of Graphs for Network Security with Real-Time Edge Learning

We present a novel approach in network security using unsupervised online machine learning method at the edge, through graph learning. The proposed system takes advantage of an online learning paradigm, by collecting real network data to build a ground truth of a network's topology, using shallow graph neural networks (GNNs). Our proposed solution includes an edge-based infrastructure, through K3s and Kafka, which could then scale to match the needs of larger networks. We then perform simple cyber-attacks and show how visual analysis can identify malicious behaviors, without any prior labeled data. Our results against simple attacks show promise that improved graph analytics should capture even more complex attack vectors. We then conclude with some suggestions for improved edge deployment, against larger and more complex networks.

• Year: 2023
• Category: Artificial Intelligence
• Tag: Intrusion Detection Systems, Network Security, Artificial Intelligence, Machine Learning
• Author: Michael M. Jerge, Virgil O. Barnard, Grant Fullenkamp, Andrew Klawa
• Released: ICNCC '23: Proceedings of the 2023 12th International Conference on Networks, Communication and Computing