Biblio

For the purpose of stealthiness, trigger-based Hardware Trojans(HTs) tend to have at least one trigger signal with an extremely low transition probability to evade the functional verification. In this paper, we discuss the correlation between poor testability and low transition probability, and then propose a kind of systematic Trojan trigger model with extremely low transition probability but reasonable testability, which can disable the Controllability and Observability for hardware Trojan Detection (COTD) technique, an efficient HT detection method based on circuits testability. Based on experiments and tests on circuits, we propose that the more imbalanced 0/1-controllability can indicate the lower transition probability. And a trigger signal identification method using the imbalanced 0/1-controllability is proposed. Experiments on ISCAS benchmarks show that the proposed method can obtain a 100% true positive rate and average 5.67% false positive rate for the trigger signal.

There is an increasing concern about possible hostile modification done to ICs, which are used in various critical applications. Such malicious modifications are referred to as Hardware Trojan. A novel procedure to detect these malicious Trojans using Genetic algorithm along with the logical masking technique which masks the Trojan module when embedded is presented in this paper. The circuit features such as transition probability and SCOAP are used as suitable parameters to identify the rare nodes which are more susceptible for Trojan insertion. A set of test patterns called optimal test patterns are generated using Genetic algorithm to claim that these test vectors are more feasible to detect the presence of Trojan in the circuit under test. The proposed methodologies are validated in accordance with ISCAS '85 and ISCAS '89 benchmark circuits. The experimental results proven that it achieves maximum Trigger coverage, Trojan coverage and is also able to successfully mask the inserted Trojan when it is triggered by the optimal test patterns.

With the development of cloud computing technology in the Internet of Things (IoT), the trajectory privacy in location-based services (LBSs) has attracted much attention. Most of the existing work adopts point-to-point and centralized models, which will bring a heavy burden to the user and cause performance bottlenecks. Moreover, previous schemes did not consider both online and offline trajectory protection and ignored some hidden background information. Therefore, in this paper, we design a trajectory protection scheme based on fog computing and k-anonymity for real-time trajectory privacy protection in continuous queries and offline trajectory data protection in trajectory publication. Fog computing provides the user with local storage and mobility to ensure physical control, and k-anonymity constructs the cloaking region for each snapshot in terms of time-dependent query probability and transition probability. In this way, two k-anonymity-based dummy generation algorithms are proposed, which achieve the maximum entropy of online and offline trajectory protection. Security analysis and simulation results indicate that our scheme can realize trajectory protection effectively and efficiently.