Cybersecurity

Industry-wide integration of digital and physical components has led to the development of the so-called cyber-physical framework and industrial control systems (ICS). Such capabilities have unfortunately opened up an avenue of cyber threats that have wide-ranging implications of industrial security. Examples such as Stuxnet, the 2015 Ukrainian grid attack, the 2000 Maroochy-Shire sewage spill in Australia etc. have showcased the capabilities of determined attackers to cause massive physical damage to the systems. As with any system, source of threats range from external state-sponsored and resourceful attackers to disgruntled insiders that have privileged access. Therefore, there is a need to protect such systems at the process-level in addition to traditional IT-based defenses like firewalls, encryption etc. Here, at CYNICS, data-driven and physics-based signatures have been developed for the purposes of intrusion and anomaly detection via machine-learning (ML) and other predictive modeling techniques. Additionally, weaknesses of deep learning such as adversarial attacks are also considered in the development of these signatures.