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IET Information Security is an open access journal, and articles will be immediately available to read and reuse upon publication.
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IET Information Security publishes original research and review articles in the areas of information security and cryptography.
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Chief Editor Prof. Yvo Desmedt is an expert in cryptography, a field that started as the use of coded language to transmit important messages and has since become a discipline that relies heavily on maths and computer science skills to protect the privacy and integrity of communications.
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More articlesHA-Med: A Blockchain-Based Solution for Sharing Medical Data with Hidden Policies and Attributes
Existing healthcare data-sharing solutions often combine attribute-based encryption techniques with blockchain technology to achieve fine-grained access control. However, the transparency of blockchain technology may introduce potential risks of exposing access structures and user attributes. To address these concerns, this paper proposes a novel healthcare data-sharing scheme called HA-Med. By leveraging blockchain technology, HA-Med ensures the concealment of access policies and attributes, providing a secure solution for fine-grained access control of medical data. Furthermore, the scheme supports attribute revocation and forward secrecy to enhance user privacy. The security of HA-Med is rigorously verified through theoretical analysis, and its feasibility is demonstrated through experiments conducted using the Java-based JPBC library.
DHRCA: A Design of Security Architecture Based on Dynamic Heterogeneous Redundant for System on Wafer
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.
Deep Learning in Cybersecurity: A Hybrid BERT–LSTM Network for SQL Injection Attack Detection
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.
Differential Fault Attacks on Privacy Protocols Friendly Symmetric-Key Primitives: RAIN and HERA
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.
A Second Preimage Attack on the XOR Hash Combiner
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 .
VulMPFF: A Vulnerability Detection Method for Fusing Code Features in Multiple Perspectives
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.