Position-Verification in Multi-Channel Models

We propose an collusion-attack-resistant position-verification protocol in a new model called multi-channel model. In the multi-channel model, there are lots of communication channels. When a player picks a random channel and sends a short message over it, the message might slip by an adversary with high probability if the adversary does not know the channel beforehand. This idea is motivated from the spread spectrum communication techniques. We adopt it to solve the position-verification task. Adding different constraints into the multi-channel model, we make three sub-models: receiving-constrained multi-channel model, sending-constrained multi-channel model and cover-constrained multi-channel model. Our position-verification protocol is secure under all of these sub-models with appropriate parameters

A generic approach for the automatic verification of featured, parameterised systems

A general technique is presented that allows property based feature analysis of systems consisting of an arbitrary number of components. Each component may have an arbitrary set of safe features. The components are defined in a guarded command form and the technique combines model checking and abstraction. Features must fulfill certain criteria in order to be safe, the criteria express constraints on the variables which occur in feature guards. The main result is a generalisation theorem which we apply to a well known example: the ubiquitous, featured telephone system

Symbolic Abstractions for Quantum Protocol Verification

Quantum protocols such as the BB84 Quantum Key Distribution protocol exchange qubits to achieve information-theoretic security guarantees. Many variants thereof were proposed, some of them being already deployed. Existing security proofs in that field are mostly tedious, error-prone pen-and-paper proofs of the core protocol only that rarely account for other crucial components such as authentication. This calls for formal and automated verification techniques that exhaustively explore all possible intruder behaviors and that scale well. The symbolic approach offers rigorous, mathematical frameworks and automated tools to analyze security protocols. Based on well-designed abstractions, it has allowed for large-scale formal analyses of real-life protocols such as TLS 1.3 and mobile telephony protocols. Hence a natural question is: Can we use this successful line of work to analyze quantum protocols? This paper proposes a first positive answer and motivates further research on this unexplored path

Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading