Abstract
The advent of computer networks and the Internet has drastically altered the means by which we share information & interact with each other. However, this technological advancement has also created room for malevolent behaviour where individuals exploit weak points with the intent of gaining access to confidential data, blocking activity etc. To this end, intrusion detection systems (IDS) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparison with previous attacks. However, with the increased availability of computational power & data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we aim to explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time-series format and use predictive models to forecast forthcoming malign packets. The findings indicate that our model performs strongly, exhibiting accuracy that is within a 4% margin when compared to conventional real-time detection.
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Acknowledgements
This work was funded by the H2020 CyberSEAS project, contract no. 101020560, within the H2020 Framework Program of the European Commission.
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Psychogyios, K., Bourou, S., Papadakis, A., Nikolaou, N., Zahariadis, T. (2023). Time-Series Modeling for Intrusion Detection Systems. In: Ossowski, S., Sitek, P., Analide, C., Marreiros, G., Chamoso, P., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 20th International Conference. DCAI 2023. Lecture Notes in Networks and Systems, vol 740. Springer, Cham. https://doi.org/10.1007/978-3-031-38333-5_1
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DOI: https://doi.org/10.1007/978-3-031-38333-5_1
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