title: Defense Against Chip Cloning Attacks Based on Fractional Hopfield Neural Networks.
abstract: This paper presents a state-of-the-art application of fractional hopfield neural networks (FHNNs) to defend against chip cloning attacks, and provides insight into the reason that the proposed method is superior to physically unclonable functions (PUFs). In the past decade, PUFs have been evolving as one of the best types of hardware security. However, the development of the PUFs has been somewhat limited by its implementation cost, its temperature variation effect, its electromagnetic interference effect, the amount of entropy in it, etc. Therefore, it is imperative to discover, through promising mathematical methods and physical modules, some novel mechanisms to overcome the aforementioned weaknesses of the PUFs. Motivated by this need, in this paper, we propose applying the FHNNs to defend against chip cloning attacks. At first, we implement the arbitrary-order fractor of a FHNN. Secondly, we describe the implementation cost of the FHNNs. Thirdly, we propose the achievement of the constant-order performance of a FHNN when ambient temperature varies. Fourthly, we analyze the electrical performance stability of the FHNNs under electromagnetic disturbance conditions. Fifthly, we study the amount of entropy of the FHNNs. Lastly, we perform experiments to analyze the pass-band width of the fractor of an arbitrary-order FHNN and the defense against chip cloning attacks capability of the FHNNs. In particular, the capabilities of defense against chip cloning attacks, anti-electromagnetic interference, and anti-temperature variation of a FHNN are illustrated experimentally in detail. Some significant advantages of the FHNNs are that their implementation cost is considerably lower than that of the PUFs, their electrical performance is much more stable than that of the PUFs under different temperature conditions, their electrical performance stability of the FHNNs under electromagnetic disturbance conditions is much more robust than that of the PUFs, and their amount of entropy is significantly higher than that of the PUFs with the same rank circuit scale.
authors: 1 College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors: Yi-Fei
authors: YF
authors: Pu
authors: 1 College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors: Zhang
authors: Z
authors: Yi
authors: 1 College of Computer Science, Sichuan University, Chengdu 610065, P. R. China.
authors: Ji-Liu
authors: JL
authors: Zhou
mesh_terms: D016494:Computer Security / Q000191:economics; D003201:Computers / Q000191:economics; D055615:Electrical Equipment and Supplies; D004574:Electromagnetic Fields; D019277:Entropy; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013696:Temperature
keywords: Defense against chip cloning attacks; fractance; fractional Hopfield neural networks; fractional calculus; physically unclonable function