Analyzing Autoencoder-Based Intrusion Detection System Performance
Seiba Alhassan,
Gaddafi Abdul-Salaam,
Michael Asante
et al.
Abstract:The rise in cyberattacks targeting critical network infrastructure has spurred an increased emphasis on the development of robust cybersecurity measures. In this context, there is a growing exploration of effective Intrusion Detection Systems (IDS) that leverage Machine Learning (ML) and Deep Learning (DL), with a particular emphasis on autoencoders. Recognizing the pressing need to mitigate cyber threats, our study underscores the crucial importance of advancing these methodologies. Our study aims to identify… Show more
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