Biblio

Filters: Author is Daniel Chen, University of Illinois at Urbana-Champagin  [Clear All Filters]
2016-11-18
Hui Lin, University of Illinois at Urbana-Champaign, Homa Alemzadeh, IBM TJ Watson, Daniel Chen, University of Illinois at Urbana-Champagin, Zbigniew Kalbarczyk, University of Illinois at Urbana-Champaign, Ravishankar Iyer, University of Illinois at Urbana-Champaign.  2016.  Safety-critical Cyber-physcial Attacks: Analysis, Detection, and Mitigation (poster).

Presented at NSA Science of Security Quarterly Lablet Meeting, July 2016.

2016-10-24
Hui Lin, University of Illinois at Urbana-Champaign, Homa Alemzadeh, IBM TJ Watson, Daniel Chen, University of Illinois at Urbana-Champagin, Zbigniew Kalbarczyk, University of Illinois at Urbana-Champaign, Ravishankar Iyer, University of Illinois at Urbana-Champaign.  2016.  Safety-critical Cyber-physical Attacks: Analysis, Detection, and Mitigation.
2016-11-15
Hui Lin, University of Illinois at Urbana-Champaign, Homa Alemzadeh, IBM TJ Watson, Daniel Chen, University of Illinois at Urbana-Champagin, Zbigniew Kalbarczyk, University of Illinois at Urbana-Champaign, Ravishankar K. Iyer, University of Illinois at Urbana-Champaign.  2016.  Safety-critical Cyber-physical Attacks: Analysis, Detection, and Mitigation. Symposium and Bootcamp for the Science of Security (HotSoS 2016).

Today's cyber-physical systems (CPSs) can have very different characteristics in terms of control algorithms, configurations, underlying infrastructure, communication protocols, and real-time requirements. Despite these variations, they all face the threat of malicious attacks that exploit the vulnerabilities in the cyber domain as footholds to introduce safety violations in the physical processes. In this paper, we focus on a class of attacks that impact the physical processes without introducing anomalies in the cyber domain. We present the common challenges in detecting this type of attacks in the contexts of two very different CPSs (i.e., power grids and surgical robots). In addition, we present a general principle for detecting such cyber-physical attacks, which combine the knowledge of both cyber and physical domains to estimate the adverse consequences of malicious activities in a timely manner.