Evaluating Cascading Impact of Attacks on Resilience of Industrial Control Systems: A Design-Centric Modeling Approach

A design-centric modeling approach was proposed to model the behaviour of the physical processes controlled by Industrial Control Systems (ICS) and study the cascading impact of data-oriented attacks. A threat model was used as input to guide the construction of the CPS model where control components which are within the adversary's intent and capabilities are extracted. The relevant control components are subsequently modeled together with their control dependencies and operational design specifications. The approach was demonstrated and validated on a water treatment testbed. Attacks were simulated on the testbed model where its resilience to attacks was evaluated using proposed metrics such as Impact Ratio and Time-to-Critical-State. From the analysis of the attacks, design strengths and weaknesses were identified and design improvements were recommended to increase the testbed's resilience to attacks

Jacobian Ensembles Improve Robustness Trade-offs to Adversarial Attacks

Deep neural networks have become an integral part of our software infrastructure and are being deployed in many widely-used and safety-critical applications. However, their integration into many systems also brings with it the vulnerability to test time attacks in the form of Universal Adversarial Perturbations (UAPs). UAPs are a class of perturbations that when applied to any input causes model misclassification. Although there is an ongoing effort to defend models against these adversarial attacks, it is often difficult to reconcile the trade-offs in model accuracy and robustness to adversarial attacks. Jacobian regularization has been shown to improve the robustness of models against UAPs, whilst model ensembles have been widely adopted to improve both predictive performance and model robustness. In this work, we propose a novel approach, Jacobian Ensembles-a combination of Jacobian regularization and model ensembles to significantly increase the robustness against UAPs whilst maintaining or improving model accuracy. Our results show that Jacobian Ensembles achieves previously unseen levels of accuracy and robustness, greatly improving over previous methods that tend to skew towards only either accuracy or robustness

Heat Transfer from an Immersed Tube in a Bubbling Fluidized Bed

An Eulerian–Eulerian approach was used to investigate the effects of particle size and immersed tube temperature on bubbling and heat transfer behaviors in a gas fluidized bed. Large gas bubbles were observed to split into smaller bubbles that flowed around the immersed tube during the fluidization process. The formation of pockets of gas around the immersed tube led to a lower heat transfer coefficient. Heat transfer between the immersed tube and particles was facilitated by a phenomenon of particle renewal. Larger gas bubbles formed in the gas fluidized bed containing larger particles and this resulted in lower heat transfer coefficients due to the formation of more gas pockets around the immersed tube. When the temperature of the immersed tube was increased, the sensitivity of the heat transfer process toward formation of gas pockets around the immersed tube was observed to increase