The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,etc.But its manufacturing process exists unreliability,so cryptographic chips must b...The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,etc.But its manufacturing process exists unreliability,so cryptographic chips must be rigorously tested.Due to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated circuits.However,while providing good controllability and observability,it also provides attackers with a backdoor to steal keys.In the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan cells.If the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan data.The proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.展开更多
As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools...As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.展开更多
基金This work was funded by the Researchers Supporting Project No.(RSP2022R509)King Saud University,Riyadh,Saudi Arabia.In additionthe Natural Science Foundation of Hunan Province under Grant no.2020JJ5604,2022JJ2029 and 2020JJ4622the National Natural Science Foundation of China under Grant no.62172058.
文摘The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,etc.But its manufacturing process exists unreliability,so cryptographic chips must be rigorously tested.Due to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated circuits.However,while providing good controllability and observability,it also provides attackers with a backdoor to steal keys.In the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan cells.If the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan data.The proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.
文摘As the internet of things(IoT)continues to expand rapidly,the significance of its security concerns has grown in recent years.To address these concerns,physical unclonable functions(PUFs)have emerged as valuable tools for enhancing IoT security.PUFs leverage the inherent randomness found in the embedded hardware of IoT devices.However,it has been shown that some PUFs can be modeled by attackers using machine-learning-based approaches.In this paper,a new deep learning(DL)-based modeling attack is introduced to break the resistance of complex XAPUFs.Because training DL models is a problem that falls under the category of NP-hard problems,there has been a significant increase in the use of meta-heuristics(MH)to optimize DL parameters.Nevertheless,it is widely recognized that finding the right balance between exploration and exploitation when dealing with complex problems can pose a significant challenge.To address these chal-lenges,a novel migration-based multi-parent genetic algorithm(MBMPGA)is developed to train the deep convolutional neural network(DCNN)in order to achieve a higher rate of accuracy and convergence speed while decreas-ing the run-time of the attack.In the proposed MBMPGA,a non-linear migration model of the biogeography-based optimization(BBO)is utilized to enhance the exploitation ability of GA.A new multi-parent crossover is then introduced to enhance the exploration ability of GA.The behavior of the proposed MBMPGA is examined on two real-world optimization problems.In benchmark problems,MBMPGA outperforms other MH algorithms in convergence rate.The proposed model are also compared with previous attacking models on several simulated challenge-response pairs(CRPs).The simulation results on the XAPUF datasets show that the introduced attack in this paper obtains more than 99%modeling accuracy even on 8-XAPUF.In addition,the proposed MBMPGA-DCNN outperforms the state-of-the-art modeling attacks in a reduced timeframe and with a smaller number of required sets of CRPs.The area under the curve(AUC)of MBMPGA-DCNN outperforms other architectures.MBMPGA-DCNN achieved sensitivities,specificities,and accuracies of 99.12%,95.14%,and 98.21%,respectively,in the test datasets,establishing it as the most successful method.
