A compressive sensing algorithm for hardware trojan detection

Traditionally many fabless companies outsource the fabrication of IC design to the foundries, which may not be trusted always. In order to ensure trusted IC’s it is more significant to develop an efficient technique that detects the presence of hardware Trojan. This malicious insertion causes the logic variation in the nets or leaks some sensitive information from the chip, which reduces the reliability of the system. The conventional testing algorithm for generating test vectors reduces the detection sensitivity due to high process variations. In this work, we present a compressive sensing approach, which can significantly generate optimal test patterns compared to the ATPG vectors. This approach maximizes the probability of Trojan circuit activation, with a high level of Trojan detection rate. The side channel analysis such as power signatures are measured at different time stamps to isolate the Trojan effects. The effect of process noise is minimized by this power profile comparison approach, which provides high detection sensitivity for varying Trojan size and eliminates the requirement of golden chip. The proposed test generation approach is validated on ISCAS benchmark circuits, which achieves Trojan detection coverage on an average of 88.6% reduction in test length when compared to random pattern

Population-based incremental learning with associative memory for dynamic environments

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments