Abstract Code clone detection has garnered significant attention across various fields, including code refactoring, plagiarism detection, and software maintenance. Numerous methods have been proposed for detecting code clones; however, while text-based and token-based approaches are scalable, they often fail to consider code semantics and are unable to effectively handle semantic code clones. Although tree-based methods perform well in semantic code clone detection, they are limited by the complex structure of trees, making it challenging to apply to large-scale clone detection. Moreover, these methods struggle to achieve fine-grained semantic code clone detection, lacking the ability to pinpoint specific code blocks within semantic clones. In this paper, we propose Rator , a tree-based code clone detector that combines scalability and fine-grained analysis capabilities while effectively detecting semantic clones. Specifically, we design a tree encoding method based on node degrees of freedom, which can transform complex tree structures into simple vector representations while preserving the structural details of the tree. In this way, we can encode all the subtrees of the abstract syntax tree into separate sets of vectors and derive similar features by calculating the similarity between these vectors. The derived similar features serve dual purposes: firstly, they are employed to train a machine learning-based code clone detector, and then, by analyzing the subtree types corresponding to the feature values, specific clone code blocks can be precisely located, thus achieving fine-grained code clone detection. Experimental results show that Rator outperforms nine state-of-the-art code clone detectors with F1 scores of 0.99 and 0.91 on BigCloneBench and Google Code Jam datasets, respectively. As for scalability, Rator is about 93 times faster than ASTNN , another state-of-the-art tree-based semantic clone detector. Regarding fine-grained detection, Rator correctly identifies the concrete clone block with a Top-3 ranked list. Furthermore, the accuracy of fine-grained detection on the Google Code Jam dataset is up to 100% with a Top-2 ranked list.

To address the issues of high computational overhead, vulnerability to physical capture attacks, and insufficient in offering quantum resistance in traditional blockchain-based cross-domain authentication protocols for collaborative unmanned aerial vehicles (UAVs) missions, we propose APQE-CDA, a PUF-based post-quantum end-to-end cross-domain authentication protocol. The proposed protocol employs a lightweight bucket shifter PUF (BS-PUF) to reduce the computational overhead of UAVs while providing resilience against physical capture attacks. Static Random-Access Memory (SRAM) PUF-enhanced Kyber post-quantum cryptography is utilized to ensure secure quantum resistance. In APQE-CDA, we exploit the reversibility of BS-PUF to facilitate secure Challenge Response Pair (CRP) storage on the blockchain while achieving lightweight identity authentication through commutativity. We integrate SRAM PUF randomness into the Kyber key generation mechanism to eliminate key storage on UAVs while ensuring quantum-resistant security in the authentication interactions. Finally, Burrows Abadi Needham (BAN) logic, Real-or-Random (ROR), and informal security analysis are adopted to demonstrate the security of the proposed scheme. Experiments conducted on the Raspberry Pi 5 and FPGA platforms show superior computational efficiency, lower power consumption, and enhanced quantum-resistant security compared to existing solutions. Furthermore, extensive validation through the NS3 network simulator and Hyperledger Fabric framework substantiates the protocol’s authentication efficiency and practical viability in real environment conditions.

Abstract Physical Unclonable Functions (PUFs) are novel circuit structures that provide hardware security solutions in application areas such as chip design and IoT, due to characteristics of their lightweight, key-free and tamper-resistant. PUFs are not immune to threats like machine learning modeling attacks and side channel modeling attacks. Strong PUFs are susceptible to classical machine learning attacks, however, machine learning’s effectiveness in attacking complex structured strong PUFs is limited, and its efficiency is relatively low. Side-channel modeling attacks, on the other hand, incur high implementation costs. Hence, employing deep learning for modeling attacks becomes an effective and cost-efficient choice when attacking complex structured PUFs. In this paper, we introduce a method that employs deep neural network to assess the modeling resilience of combination logic operation-based PUFs with APUFs as components for the first time. We employed a 4-layer DNN model to investigate the security resilience of PUF models involving any combination of OR AND and XOR logical operations. We explored the security regular patterns of modeling resilience. We have demonstrated for the first time that bias in PUF responses can reduce or destroy the security of PUFs. OR or AND logic operations do not provide any security benefit in PUF design, while XOR operations enhance the security of PUFs.

Physical unclonable functions (PUFs) have emerged as potent hardware primitives owing to their intrinsic properties of being secret key-free, clone-proof, and lightweight. However, PUFs cannot avoid the threats of machine learning modeling and side-channel attacks (SCAs). Nevertheless, almost all attacks neglect the correlations between the mathematical model and side-channel models introduced by PUF internal parameters; thus, such attacks fail to exploit related data and struggle in modeling complex PUFs. To address this problem, we propose a framework for using auxiliary learning SCAs to model strong PUFs by learning multiple related tasks together. Side-channel information predictions are introduced as auxiliary tasks to facilitate the primary task of predicting response. The parameters hard for the primary task to learn can be shared by the auxiliary tasks that learn the same parameters more straightforwardly. Based on the proposed framework, we design a specific auxiliary learning power SCA that employs power level prediction as the auxiliary task. The proposed attack is implemented with the hard-parameter sharing and hierarchy sharing deep neural networks. Experimental results demonstrate that the proposed attack succeeds in modeling XOR APUF, MPUF, and iPUF and outperforms the state-of-the-art methods in modeling MPUF and iPUF. We evaluate the influences of task relatedness, architecture, and loss weight ratio. Furthermore, we propose a fine-grained classification-based method to generate the auxiliary task with an enhanced relationship to the primary task. According to the response, the class corresponding to a specific side-channel state is further divided into two subclasses. Experimental results demonstrate that the generated auxiliary task promotes performance and alleviates the adverse effects of improper architecture and parameters.