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Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification
April 10, 2024, 4:11 a.m. | Jacopo Talpini, Fabio Sartori, Marco Savi
cs.CR updates on arXiv.org arxiv.org
Abstract: The evolution of Internet and its related communication technologies have consistently increased the risk of cyber-attacks. In this context, a crucial role is played by Intrusion Detection Systems (IDSs), which are security devices designed to identify and mitigate attacks to modern networks. Data-driven approaches based on Machine Learning (ML) have gained more and more popularity for executing the classification tasks required by signature-based IDSs. However, typical ML models adopted for this purpose do not properly …
arxiv attacks communication context cs.cr cs.lg cyber data data-driven detection devices identify idss internet intrusion intrusion detection network network intrusion networks quantification risk role security systems technologies trustworthiness uncertainty
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