基于遗传算法的少态节点活性提升方法

A rare node activity improvement method based on genetic algorithm

为了进一步提高对硬件木马的识别水平,利用遗传算法的全局搜索能力,提出基于遗传算法的少态节点活性提升方法,以少态节点的翻转次数代表活性,寻找能提升少态节点活性的测试向量集.在测试向量激励下,将被测电路中少态节点的翻转次数作为适应度,在整个测试向量空间内进行选择、交叉和变异操作,并比较父代与子代适应度,保留适应度较大的向量集,最终达到迭代终止条件,生成优化的测试向量集.以ISCAS'85基准电路c3540为研究对象进行仿真验证,实验结果表明,在算法运行前,以1000个向量为输入时,电路所有非少态节点的翻转次数之和为155158,少态节点的翻转次数之和为117;在算法运行后,以1000个向量为输入时,电路所有非少态节点的翻转次数之和为157146,少态节点的翻转次数之和为882.遗传算法生成的测试向量组将少态节点的翻转率提高了7.54倍,并将相对翻转率提升了7.44倍.

In order to improve the identification level of hardware Trojan,a rare node activity improvement method based on genetic algorithm was proposed,by using the global searching ability of genetic algorithm.The proposed method was used to search the test vector sets which could improve the activity of rare nodes,by taking the toggle count of rare nodes as activity.Under the excitation of the test vector,the transition count of rare nodes in device under test(DUT)was taken as fitness,and the operations of selection,crossover and mutation were conducted during the whole test vector space.The fitness of parent and the fitness of offspring were compared,and the higher fitness test vector sets were saved,and eventually the optimized test vector set was generated by iteration when the iteration termination condition was reached.Simulation verification was conducted with the ISCAS'85 benchmark circuit c3540 as research object.Results showed that before the operation of the algorithm,when 1 000 vectors were used as input,the sum of the transition times of the un-rare nodes was 155 158,and the sum of the transition times of the rare nodes was 117.After the operation of the algorithm,when 1 000 vectors were used as input,the sum of the transition times of the un-rare nodes was 157 146,and the sum of the transition times of the rare nodes was 882.The test vectors generated by genetic algorithm improved the transition rate of rare nodes up to 7.54 times,and increased the relative transition rate of rare nodes up to 7.44 times.