Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification

arXiv:2310.10655v2 Announce Type: replace
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