Poisoning attacks on cyber attack detectors for industrial control systems
Biggio, Battista;
2021-01-01
Abstract
Recently, neural network (NN)-based methods, including autoencoders, have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the natural evolution (i.e., concept drift) of the monitored signals. However, by exploiting this mechanism, an attacker can fake the signals provided by corrupted sensors at training time and poison the learning process of the detector such that cyber attacks go undetected at test time. With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online NN detectors. We propose two distinct attack algorithms, namely, interpolation- and back-gradient based poisoning, and demonstrate their effectiveness on both synthetic and real-world ICS data. We also discuss and analyze some potential mitigation strategies.File | Size | Format | |
---|---|---|---|
kravchik21-sac-editorial.pdf Solo gestori archivio
Type: versione editoriale
Size 796.92 kB
Format Adobe PDF
|
796.92 kB | Adobe PDF | & nbsp; View / Open Request a copy |
kravchik21-sac.pdf open access
Type: versione pre-print
Size 1.4 MB
Format Adobe PDF
|
1.4 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.