Machine Learning for Network Intrusion Detection
نویسنده
چکیده
In recent years, networks have become an increasingly valuable target of malicious attacks due to the increased amount of user data they contain. In defense, Network Intrusion Detection Systems (NIDSs) have been developed to detect and report suspicious activity (i.e. an attack). In this project, we explore unsupervised learning techniques for building NIDs, which only analyze unencrypted packet header fields and can run online.
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تاریخ انتشار 2017