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Cryptographic hash functions have become the basis of modern network computing for identity authorization and secure computing; protocol consistency of cryptographic hash functions is one of the most important properties that affect the security and correctness of cryptographic implementations, and protocol consistency should be well proven before being applied in practice. Software verification has seen substantial application in safety-critical areas and has shown the ability to deliver better quality assurance for modern software; thus, applying software verification to a protocol consistency proof for cryptographic hash functions is a reasonable approach to prove their correctness. Verification of protocol consistency of cryptographic hash functions includes modeling of the cryptographic protocol and program analysis of the cryptographic implementation; these require a dedicated cryptographic implementation model that preserves the semantics of the code, efficient analysis of cryptographic operations on arrays and bits, and the ability to verify large-scale implementations. In this paper, we propose a fully automatic software verification framework, VeriHash, that brings software verification to protocol consistency proofs for cryptographic hash function implementations. It solves the above challenges by introducing a novel cryptographic model design for modeling the semantics of cryptographic hash function implementations, extended array theories for analysis of operations, and compositional verification for scalability. We evaluated our verification framework on two SHA-3 cryptographic hash function implementations: the winner of the NIST SHA-3 competition, Keccack; and an open-source hash program, RHash. We successfully verified the core parts of the two implementations and reproduced a bug in the published edition of RHash.

Time-triggered systems play an important role in industrial embedded systems. The time-triggered network is deployed on the time-triggered network-on-chip implementation. It ensures the safety-critical industrial communication for real-time embedded multiprocessor systems. To guarantee the safety-critical requirements for communication, each message is transmitted by a predefined static schedule. However, synthesizing a feasible schedule is a challenge because both spatial and temporal constraints should be considered. This article presents a novel memetic-based schedule synthesis algorithm to derive a feasible schedule by determining the offset of messages on the time-triggered network-on-chip. Memetic-based schedule synthesis algorithm is based on memetic algorithm, which incorporates local search in the iterations of general genetic algorithm. We compare memetic-based schedule synthesis algorithm with genetic algorithm in different scale of time-triggered network-on-chip and number of messages. The experimental results show that the memetic-based schedule synthesis algorithm is effective to synthesize a feasible schedule, and the failure schedule synthesized by memetic-based schedule synthesis algorithm is only 34.2% in average compared to the conventional genetic algorithm.

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.