VICRA: Variance-Invariance-Covariance Regularization for Attack Prediction

Abstract

In cybersecurity, accurate and timely prediction of attacks plays a crucial role in mitigating the risks and impacts of cyber threats. However, traditional attack prediction methods that rely on training Machine Learning (ML) algorithms directly on raw data often suffer from high false alarm rates and low detection rates, leading to inaccurate and unreliable results. To overcome these limitations, this paper presents a novel approach that integrates attack prediction with self-supervision using variance-invariance-covariance regularization (VICReg). The proposed method harnesses VICReg to enhance raw data and generate representations while leveraging self-supervision to learn meaningful features without supervision. Training classic ML algorithms on these refined representations improves prediction accuracy and enhances the robustness of the learning process. We provide a comprehensive description of the proposed method and present an evaluation of its performance on several benchmark datasets. The experimental results demonstrate the superiority of the proposed method over classic ML algorithms.

@inproceedings{FedCSIS20231151,
	author={Aditya Srinivas Menon and Gouri Nair},
	pages={1075–1080},
	title={VICRA: Variance-Invariance-Covariance Regularization for Attack Prediction},
	booktitle={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
	year={2023},
	editor={Maria Ganzha and Leszek Maciaszek and Marcin Paprzycki and Dominik Ślęzak},
	publisher={IEEE},
	doi={10.15439/2023F1151},
	url={http://dx.doi.org/10.15439/2023F1151},
	volume={35},
	series={Annals of Computer Science and Information Systems}
}