  Industrial control systems (ICS), which in many cases are components of
critical national infrastructure, are increasingly being connected to other
networks and the wider internet motivated by factors such as enhanced
operational functionality and improved efficiency. However, set in this
context, it is easy to see that the cyber attack surface of these systems is
expanding, making it more important than ever that innovative solutions for
securing ICS be developed and that the limitations of these solutions are well
understood. The development of anomaly based intrusion detection techniques has
provided capability for protecting ICS from the serious physical damage that
cyber breaches are capable of delivering to them by monitoring sensor and
control signals for abnormal activity. Recently, the use of so-called stealthy
attacks has been demonstrated where the injection of false sensor measurements
can be used to mimic normal control system signals, thereby defeating anomaly
detectors whilst still delivering attack objectives. In this paper we define a
deep learning-based framework which allows an attacker to conduct stealthy
attacks with minimal a-priori knowledge of the target ICS. Specifically, we
show that by intercepting the sensor and/or control signals in an ICS for a
period of time, a malicious program is able to automatically learn to generate
high-quality stealthy attacks which can achieve specific attack goals whilst
bypassing a black box anomaly detector. Furthermore, we demonstrate the
effectiveness of our framework for conducting stealthy attacks using two
real-world ICS case studies. We contend that our results motivate greater
attention on this area by the security community as we demonstrate that
currently assumed barriers for the successful execution of such attacks are
relaxed.
