7 results found
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Feng C, Li T, Zhu Z, et al., A Deep Learning-based Framework for Conducting Stealthy Attacks in Industrial Control Systems
Industrial control systems (ICS), which in many cases are components ofcritical national infrastructure, are increasingly being connected to othernetworks and the wider internet motivated by factors such as enhancedoperational functionality and improved efficiency. However, set in thiscontext, it is easy to see that the cyber attack surface of these systems isexpanding, making it more important than ever that innovative solutions forsecuring ICS be developed and that the limitations of these solutions are wellunderstood. The development of anomaly based intrusion detection techniques hasprovided capability for protecting ICS from the serious physical damage thatcyber breaches are capable of delivering to them by monitoring sensor andcontrol signals for abnormal activity. Recently, the use of so-called stealthyattacks has been demonstrated where the injection of false sensor measurementscan be used to mimic normal control system signals, thereby defeating anomalydetectors whilst still delivering attack objectives. In this paper we define adeep learning-based framework which allows an attacker to conduct stealthyattacks with minimal a-priori knowledge of the target ICS. Specifically, weshow that by intercepting the sensor and/or control signals in an ICS for aperiod of time, a malicious program is able to automatically learn to generatehigh-quality stealthy attacks which can achieve specific attack goals whilstbypassing a black box anomaly detector. Furthermore, we demonstrate theeffectiveness of our framework for conducting stealthy attacks using tworeal-world ICS case studies. We contend that our results motivate greaterattention on this area by the security community as we demonstrate thatcurrently assumed barriers for the successful execution of such attacks arerelaxed.
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