#Savage xr auto attack software#

#Savage xr auto attack software#

In Proceedings of the Open-Source Software workshop at the International Conference on Robotics and Automation (ICRA), volume 3, 2009. ROS: an open-source robot operating system. Attack detection and identification in cyber-physical systems. IEEE Transactions on Industrial Informatics, 10(1):3-16, 2014. Model-driven safety analysis of closed-loop medical systems. 2nd ACM international conference on High Confidence Networked Systems, pages 75-76, 2013.

Towards synthesis of platform-aware attack-resilient control systems. Building Software for Simulation: Theory and Algorithms, with Applications in C++. In 2010 IEEE Symposium on Security and Privacy (SP), pages 447-462, 2010. Experimental security analysis of a modern automobile. Proceedings of the IEEE, Special Issue on Technology of Networked Control Systems, 95(1):138-162, 2007. A survey of recent results in networked control systems. Secure estimation and control for cyber-physical systems under adversarial attacks. In Proceedings of the 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pages 337-344, 2011. Secure state-estimation for dynamical systems under active adversaries. In Proceedings of USENIX Security Symposium, 2011. Comprehensive experimental analyses of automotive attack surfaces. 3rd USENIX Workshop on Hot topics in security, 2008. Research challenges for the security of control systems. 'Spoofers' Use Fake GPS Signals to Knock a Yacht Off Course.Finally, we illustrate and experimentally evaluate this approach on an unmanned ground vehicle case-study. This enables mapping control performance requirements into real-time (i.e., timing related) specifications imposed on the underlying platform. Furthermore, we describe how implementation issues such as jitter, latency and synchronization errors can be mapped into parameters of the state estimation procedure that describe modeling errors, and provide a bound on the state-estimation error caused by modeling errors. When the the estimated states are used by a state-based feedback controller, we show that the attacker cannot destabilize the system by exploiting the difference between the model used for the state estimation and the real physical dynamics of the system. Consequently, in this work we present a method for state estimation in presence of attacks, for systems with noise and modeling errors. Yet, the existing state-estimators are based on the non-realistic assumption that the exact system model is known. This has motivated the need for attack-resilient state estimation. The interaction between information technology and physical world makes Cyber-Physical Systems (CPS) vulnerable to malicious attacks beyond the standard cyber attacks.