IET Information Security

System on Wafer (SoW) based on chiplets may be implanted with hardware Trojans (HTs) by untrustworthy third-party chiplet vendors. However, traditional HTs protection techniques cannot guarantee complete protection against HTs, which poses a great challenge to the hardware security of SoW. In this paper, we propose a computing architecture based on endogenous security theory—dynamic heterogeneous redundant computing architecture (DHRCA) that can tolerate and detect HTs at runtime. The security of our approach is analyzed by building a generalized stochastic coloring petri net (GSCPN) model of DHRCA. The simulation results based on the GSCPN model show that our method can improve the system security probability to 0.8690 and the system availability probability to 0.9750 in the steady state compared with typical triple-mode redundancy and runtime monitoring methods. Furthermore, the impact of different attack and defense strategies on system security of different methods is simulated and analyzed in this paper.

In the past decade, cybersecurity has become increasingly significant, driven largely by the increase in cybersecurity threats. Among these threats, SQL injection attacks stand out as a particularly common method of cyber attack. Traditional methods for detecting these attacks mainly rely on manually defined features, making these detection outcomes highly dependent on the precision of feature extraction. Unfortunately, these approaches struggle to adapt to the increasingly sophisticated nature of these attack techniques, thereby necessitating the development of more robust detection strategies. This paper presents a novel deep learning framework that integrates Bidirectional Encoder Representations from Transformers (BERT) and Long Short-Term Memory (LSTM) networks, enhancing the detection of SQL injection attacks. Leveraging the advanced contextual encoding capabilities of BERT and the sequential data processing ability of LSTM networks, the proposed model dynamically extracts word and sentence-level features, subsequently generating embedding vectors that effectively identify malicious SQL query patterns. Experimental results indicate that our method achieves accuracy, precision, recall, and F1 scores of 0.973, 0.963, 0.962, and 0.958, respectively, while ensuring high computational efficiency.

As the practical applications of fully homomorphic encryption (FHE), secure multi-party computation (MPC) and zero-knowledge (ZK) proof continue to increase, so does the need to design and analyze new symmetric-key primitives that can adapt to these privacy-preserving protocols. These designs typically have low multiplicative complexity and depth with the parameter domain adapted to their application protocols, aiming to minimize the cost associated with the number of nonlinear operations or the multiplicative depth of their representation as circuits. In this paper, we propose two differential fault attacks against a one-way function RAIN used for Rainier (CCS 2022), a signature scheme based on the MPC-in-the-head approach and an FHE-friendly cipher HERA used for the RtF framework (Eurocrypt 2022), respectively. We show that our attacks can recover the keys for both ciphers by only injecting a fault into the internal state and requiring only one normal and one faulty ciphertext blocks. Thus, we can use only the practical complexity of bit operations to break the full-round RAIN with 128/192/256-bit keys. For full-round HERA with 80/128-bit key, our attack is practical with complexity the complexity of encryptions with about memory.

The exclusive-or (XOR) hash combiner is a classical hash function combiner, which is well known as a good PRF and MAC combiner, and is used in practice in TLS versions 1.0 and 1.1. In this work, we analyze the second preimage resistance of the XOR combiner underlying two different narrow-pipe hash functions with weak ideal compression functions. To control simultaneously the behavior of the two different hash functions, we develop a new structure called multicollision-and-double-diamond. Multicollision-and-double-diamond structure is constructed using the idea of meet-in-the-middle technique, combined with Joux’s multicollision and Chen’s inverse-diamond structure. Then based on the multicollision-and-double-diamond structure, we present a second preimage attack on the XOR hash combiner with the time complexity of about  ( is the size of the XOR hash combiner and and are respectively the depths of the two inverse-diamond structures), less than the ideal time complexity , and memory of about .

Source code vulnerabilities are one of the significant threats to software security. Existing deep learning-based detection methods have proven their effectiveness. However, most of them extract code information on a single intermediate representation of code (IRC), which often fails to extract multiple information hidden in the code fully, significantly limiting their performance. To address this problem, we propose VulMPFF, a vulnerability detection method that fuses code features under multiple perspectives. It extracts IRC from three perspectives: code sequence, lexical and syntactic relations, and graph structure to capture the vulnerability information in the code, which effectively realizes the complementary information of multiple IRCs and improves vulnerability detection performance. Specifically, VulMPFF extracts serialized abstract syntax tree as IRC from code sequence, lexical and syntactic relation perspective, and code property graph as IRC from graph structure perspective, and uses Bi-LSTM model with attention mechanism and graph neural network with attention mechanism to learn the code features from multiple perspectives and fuse them to detect the vulnerabilities in the code, respectively. We design a dual-attention mechanism to highlight critical code information for vulnerability triggering and better accomplish the vulnerability detection task. We evaluate our approach on three datasets. Experiments show that VulMPFF outperforms existing state-of-the-art vulnerability detection methods (i.e., Rats, FlawFinder, VulDeePecker, SySeVR, Devign, and Reveal) in Acc and F1 score, with improvements ranging from 14.71% to 145.78% and 152.08% to 344.77%, respectively. Meanwhile, experiments in the open-source project demonstrate that VulMPFF has the potential to detect vulnerabilities in real-world environments.

Named Data Networking (NDN) is a promising network architecture that differs from the traditional TCP/IP network, as it focuses on data rather than the host. A new secure model is required to provide the data-oriented trust instead of the host-oriented trust. This paper proposes a new secure solution in the NDNs named Secure Mechanism supported by Certificateless Digital Signature and Blockchain (CLDS-B). The CLDS-B scheme employs a certificateless digital signature to guarantee the authentication and integrity of data. On the one hand, the key escrow problem has been solved to eliminate the risks of compromised private key generators; on the other hand, the data name has been bound to the public key to prevent the false public key. Moreover, the blockchain is used to manage cryptographic information. Each domain designates an information service entity to join the blockchain so that the consumer could retrieve the cryptographic information public parameter in the local domain if necessary. Furthermore, due to the decentralization of the blockchain, the CLDS-B would be robust to resist the single-node failure. Simulation results show that the CLDS-B scheme outperforms a classic NDN scheme, although it shows slightly inferior to the other secure NDN scheme. The security verification and analysis show that the CLDS-B would resist the key escrow attack. The CLDS-B would be a competitive solution in scenarios with a high-security level.