Biblio

Adversarial models are well-established for cryptographic protocols, but distributed real-time protocols have requirements that these abstractions are not intended to cover. The IEEE/IEC 61850 standard for communication networks and systems for power utility automation in particular not only requires distributed processing, but in case of the generic object oriented substation events and sampled value (GOOSE/SV) protocols also hard real-time characteristics. This motivates the desire to include both quality of service (QoS) and explicit network topology in an adversary model based on a π-calculus process algebraic formalism based on earlier work. This allows reasoning over process states, placement of adversarial entities and communication behaviour. We demonstrate the use of our model for the simple case of a replay attack against the publish/subscribe GOOSE/SV subprotocol, showing bounds for non-detectability of such an attack.

Resilient control systems should efficiently restore control into physical systems not only after the sabotage of themselves, but also after breaking physical systems. To enhance resilience of control systems, given an originally minimal-input controlled linear-time invariant(LTI) physical system, we address the problem of efficient control recovery into it after removing a known system vertex by finding the minimum number of inputs. According to the minimum input theorem, given a digraph embedded into LTI model and involving a precomputed maximum matching, this problem is modeled into recovering controllability of it after removing a known network vertex. Then, we recover controllability of the residual network by efficiently finding a maximum matching rather than recomputation. As a result, except for precomputing a maximum matching and the following removed vertex, the worst-case execution time of control recovery into the residual LTI physical system is linear.

In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can offer a cost-effective power management and supply alternative to national power grid connections. RCSMG architectures handle communications over distributed lossy networks to minimize operation costs. However, the unreliable nature of lossy networks makes privacy an important consideration. Existing anonymisation works on data perturbation work mainly by distortion with additive noise. Apply these solutions to RCSMGs is problematic, because deliberate noise additions must be distinguishable both from system and adversarial generated noise. In this paper, we present a brief survey of privacy risks in RCSMGs centered on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.