Network Anomaly Detection Using Federated Learning

Conference · Mon Nov 28 04:00:00 EST 2022

DOI:https://doi.org/10.1109/MILCOM55135.2022.10017793· OSTI ID:3003232

Marfo, William ^[1]; Tosh, Deepak K. ^[1]; Moore, Shirley V. ^[1]

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1. University of Texas at El Paso,Department of Computer Science,El Paso,USA

The internet is turning out to be an integral part of every-one's lives as more and more devices are being connected to serve societal needs. Our work is motivated by two ma-jor observations. Firstly, one drawback of connecting to the network is the threat of network attacks that can compromise users' private information, leading to data loss and adversely affecting productivity. There are several traditional security mechanisms to defend against these attacks, such as firewalls, virtual private networks (VPNs), demilitarized zones (DMZs), and vulnerability scanners. One way to prevent these attacks is early detection and prevention. However, these kinds of architecture do not scale very well because of their centralized nature. Secondly, we observe from heuristics and data set distributions that the majority of the requests made to a server are innocuous. Therefore, almost all server request data sets are highly imbalanced, weighted highly towards the harmless requests.

• Research Organization: National Energy Technology Laboratory
• Sponsoring Organization: Department of Energy
• DOE Contract Number: FE0032089
• OSTI ID: 3003232
• Country of Publication: United States
• Language: English