Vesper uses machine learning to profile the link with each host, and to detect when the environment changes. Using this technique, Vesper can detect MitM attacks with high accuracy, to the extent that it can distinguish between identical networking devices.
Mar 7, 2018 · Vesper uses neural networks called autoencoders to model the normal patterns of the echoed pulses, and detect when the environment changes.
Using this technique, Vesper is able to detect MitM attacks with high accuracy while incurring minimal network overhead. We evaluate Vesper on LANs consisting ...
Altogether, Vesper (1) probes a link with an end-host with a modulated ICMP excita- tion signal, (2) extracts three features from the response, and (3) detects ...
Vesper is a novel plug-and-play MitM detector for local area networks that uses neural networks called autoencoders to model the normal patterns of the ...
Vesper uses neural networks called autoencoders to model the normal patterns of the echoed pulses, and detect when the environment changes. Using this technique ...
Using this technique, Vesper is able to detect MitM attacks with high accuracy while incurring minimal network overhead. We evaluate Vesper on LANs consisting ...
Duration: 21:20
Posted: Jan 15, 2020
Posted: Jan 15, 2020
Current Detection Methods: • Don't generalize to different attacks. • Not portable (e.g., expensive NIDS). • Generate false positives.
Jun 1, 2019 · Vesper uses neural networks called autoencoders to model the normal patterns of the echoed pulses and detect when the environment changes. Using ...