A counter abstraction technique for the verification of robot swarms.

We study parameterised verification of robot swarms against temporal-epistemic specifications. We relax some of the significant restrictions assumed in the literature and present a counter abstraction approach that enable us to verify a potentially much smaller abstract model when checking a formula on a swarm of any size. We present an implementation and discuss experimental results obtained for the alpha algorithm for robot swarms

Verifying Security Properties in Unbounded Multiagent Systems

We study the problem of analysing the security for an unbounded number of concurrent sessions of a cryptographic protocol. Our formal model accounts for an arbitrary number of agents involved in a protocol-exchange which is subverted by a Dolev-Yao attacker. We define the parameterised model checking problem with respect to security requirements expressed in temporal-epistemic logics. We formulate sufficient conditions for solving this problem, by analysing several finite models of the system. We primarily explore authentication and key-establishment as part of a larger class of protocols and security requirements amenable to our methodology. We introduce a tool implementing the technique, and we validate it by verifying the NSPK and ASRPC protocols

Parameterised model checking for alternating-time temporal logic

We investigate the parameterised model checking problem for specifications expressed in alternating-time temporal logic. We introduce parameterised concurrent game structures representing infinitely many games with different number of agents. We introduce a parametric variant of ATL to express properties of the system irrespectively of the number of agents present in the system. While the parameterised model checking problem is undecidable, we define a special class of systems on which we develop a sound and complete counter abstraction technique. We illustrate the methodology here devised on the prioritised version of the train-gate-controller

Verifying Security Properties in Unbounded Multiagent Systems

We study the problem of analysing the security for an unbounded number of concurrent sessions of a cryptographic protocol. Our formal model accounts for an arbitrary number of agents involved in a protocol-exchange which is subverted by a Dolev-Yao attacker. We define the parameterised model checking problem with respect to security requirements expressed in temporal-epistemic logics. We formulate sufficient conditions for solving this problem, by analysing several finite models of the system. We primarily explore authentication and key-establishment as part of a larger class of protocols and security requirements amenable to our methodology. We introduce a tool implementing the technique, and we validate it by verifying the NSPK and ASRPC protocols

Verifying Security Properties in Unbounded Multiagent Systems

We study the problem of analysing the security for an unbounded number of concurrent sessions of a cryptographic protocol. Our formal model accounts for an arbitrary number of agents involved in a protocol-exchange which is subverted by a Dolev-Yao attacker. We define the parameterised model checking problem with respect to security requirements expressed in temporal-epistemic logics. We formulate sufficient conditions for solving this problem, by analysing several finite models of the system. We primarily explore authentication and key-establishment as part of a larger class of protocols and security requirements amenable to our methodology. We introduce a tool implementing the technique, and we validate it by verifying the NSPK and ASRPC protocols

Efficient neural network verification via layer-based semidefinite relaxations and linear cuts

We introduce an efficient and tight layer-based semidefinite relaxation for verifying local robust-ness of neural networks. The improved tightness is the result of the combination between semidefinite relaxations and linear cuts. We obtain a computationally efficient method by decomposing the semidefinite formulation into layer wise constraints. By leveraging on chordal graph decompositions, we show that the formulation here presented is provably tighter than current approaches. Experiments on a set of benchmark networks show that the approach here proposed enables the verification of more instances compared to other relaxation methods. The results also demonstrate that the SDP relaxation here proposed is one order of magnitude faster than previous SDP methods

Verification of semantic key point detection for aircraft pose estimation

We analyse Semantic Segmentation Neural Networks running on an autonomous aircraft to estimate its pose during landing. We show that automated reasoning techniques from neural network verification can be used to analyse the conditions under which the networks can operate safely, thus providing enhanced assurance guarantees on the behaviour of the over-all pose estimation systems

Dynamic Multi-Agent Systems : Conceptual Framework, Automata-Based Modelling and Verification

We study dynamic multi-agent systems (dmass). These are multi-agent systems with explicitly dynamic features, where agents can join and leave the system during the evolution. We propose a general conceptual framework for modelling such dmass and argue that it can adequately capture a variety of important and representative cases. We then present a concrete modelling framework for a large class of dmass, composed in a modular way from agents specified by means of automata-based representations. We develop generic algorithms implementing the dynamic behaviour, namely addition and removal of agents in such systems. Lastly, we state and discuss several formal verification tasks that are specific for dmass and propose general algorithmic solutions for the class of automata representable dmass

Formal analysis of neural network-based systems in the aircraft domain

Neural networks are being increasingly used for efficient decision making in the aircraft domain. Given the safety-critical nature of the applications involved, stringent safety requirements must be met by these networks. In this work we present a formal study of two neural network-based systems developed by Boeing. The Venus verifier is used to analyse the conditions under which these systems can operate safely, or generate counterexamples that show when safety cannot be guaranteed. Our results confirm the applicability of formal verification to the settings considered