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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Tackling Variable-length Sequences with High-cardinality Features in Cyber-attack Detection

DOI: http://dx.doi.org/10.15439/2023F2385

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1295–1299 ()

Full text

Abstract. Internet of Things (IoT) based systems are vulnerable to various cyber-attacks and need advanced and smart techniques in order to achieve their security. In the FedCSIS 2023 big-data competition, participants are asked to construct scoring models to detect whether anomalous operating systems were under attack by using logs from IoT devices. These log files are variable-length sequences with high cardinality features. Through in-depth and detailed analysis, we find out concise and efficient methods to handle these huge volumes, variety, and veracity of data. On the basis of this, we create detection rules using the fundamental knowledge of mathematical statistics and train gradient boosting machine (GBM) based classifier for attack detection. Experimental and competition results prove the effectiveness of our proposed methods. Our final AUC score is 0.9999 on the private leaderboard.

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