30 April 2026, Volume 4 Issue 2
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Journal of Cybersecurity. 2026, 4(2): 1-14. https://doi.org/10.20172/j.issn.2097-3136.260301
Side-channel analysis (SCA), which extracts physical information such as power consumption and electromagnetic radiation during the operation of cryptographic devices to recover secret keys, is a critical technique for evaluating the security of cryptographic modules. Deep learning is widely adopted in this field due to its powerful feature extraction capabilities. However, the applicability of emerging neural network architectures needs systematic evaluation. Capsule network (CapsNet), as an innovative architecture, are widely applied and perform well in image recognition, but its applications in side-channel analysis tasks remain limited. The suitability of CapsNet for SCA is investigated. A lightweight CapsNet architecture is designed, an exhaustive hyperparameter search is conducted, and leading degree (LD) is used as the core evaluation metric to determine optimal configurations. The performance of CapsNets is rigorously compared with state-of-the-art convolutional neural networks (CNN) based on the ASCAD dataset under identical experimental parameters. Experimental results demonstrate that CapsNets achieve only 59%~75% of the LD values of CNN under both the hamming weight (HW) and Identity leakage models. Furthermore, CapsNets exhibit slower convergence, poorer stability, and a parameter count 5 to 50 times higher than that of CNN, leading to significantly increased computational and spatial overhead. The performance gap stems from the misalignment between the spatial-semantic optimization objectives of CapsNets and the temporal pattern mining requirements of SCA. The dynamic routing mechanism has limited sensitivity to local temporal features, while excessive parameters amplify the upper bound of generalization error under limited data, thereby exacerbating overfitting risks. These results and analysis verify the inherent incompatibility between
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Journal of Cybersecurity. 2026, 4(2): 15-28. https://doi.org/10.20172/j.issn.2097-3136.260403
With the widespread use of Deep Neural Networks (DNN) in high-risk applications, the threat of implanted backdoors has increased significantly. A TrojanNet-style backdoor attack named SIS-TrojanNet is proposed, which embeds triggers into shared images using Secret Image Sharing (SIS). Compared with the original TrojanNet, this method optimizes trigger generation. It transforms the checkerboard trigger into shared images and conceals it within input samples. When a sample contains a shared image, the infected model misclassifies the input into a target label. The triggers of SIS-TrojanNet are more covert, and the pixel changes in the shared images are random. The attack can be injected into most DNN models without training. Experimental results show that SIS-TrojanNet achieves about 96.5% attack success rate while maintaining the original task accuracy. At present, existing backdoor detection methods, such as Neural Cleanse and MOTH, cannot detect the SIS-TrojanNet attack.
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Journal of Cybersecurity. 2026, 4(2): 29-49. https://doi.org/10.20172/j.issn.2097-3136.260412
Advanced persistent threat (APT) tracking based on provenance graphs was studied to address two major challenges in multilayer attack reconstruction: insufficient labels and the semantic gap between high-level cyber threat intelligence (CTI) reports and low-level causal audit events. A temporal-aware cross-modal alignment framework, named ProvLang, was developed to associate CTI reports with typed and time-stamped provenance subgraphs, thereby enabling search-based threat tracking. The framework adopted a dual-encoder architecture, in which a temporal heterogeneous graph neural network encoded provenance graphs and a security domain-specific text encoder represented CTI reports. The two modalities were jointly optimized through contrastive learning, matching, masked modeling, and a temporal causal alignment loss to preserve cross-modal semantic consistency and hierarchical order. A two-stage search pipeline was further designed to support coarse-grained retrieval and fine-grained re-ranking. Experiments were conducted on multiple transparent computing datasets from DARPA and real-world CTI sources. The results show that the framework consistently improves cross-modal search performance, particularly in scenarios involving unknown attack procedures and cross-platform propagation. The findings indicate that CTI-driven threat tracking based on temporal-aware cross-modal alignment is feasible in practice and can reduce dependence on manually constructed query graphs.
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High performance privacy preserving face authentication system based on fully homomorphic encryptionJournal of Cybersecurity. 2026, 4(2): 50-60. https://doi.org/10.20172/j.issn.2097-3136.260212
A privacy-preserving face authentication system based on fully homomorphic encryption (FHE) was investigated, with the objective of enabling cloud-side matching without disclosing facial features and reducing computation and communication overhead. Based on the CKKS (Cheon-Kim-Kim-Song) approximate homomorphic computation scheme, a three-tier architecture of client-side encryption, cloud-side homomorphic computation, and trusted authority decryption and decision was constructed to support the generation, transmission, storage, and matching of ciphertext features. For 128-dimensional facial feature matching, a multi-slot packing and batched cosine similarity evaluation workflow was designed to complete inner product and norm computations in the ciphertext domain in parallel and return only encrypted matching scores. To address the bottlenecks of homomorphic computation, Barrett modular reduction algorithm was adopted and optimized at the assembly level, and OpenMP multithreading technology and instruction-level parallelism were combined to improve the system's computational throughput. Experimental results on an Intel platform indicate that the optimized Barrett modular reduction algorithm achieves a 1.96-fold speedup over the conventional implementation. With 32 acceleration threads and specific encryption parameters (Q=15, P=1, total modulus bit-width 881), the feature ciphertext matching latency for a batch size of 128 was reduced from 68.1 ms to 11.4 ms. The results demonstrate that the system establishes a solid performance foundation for high-concurrency and real-time authentication while strictly complying with privacy-preserving constraints.
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Journal of Cybersecurity. 2026, 4(2): 61-74. https://doi.org/10.20172/j.issn.2097-3136.260405
To counter the inherent capacity-detectability trade-off of traditional steganography, we present an implicit neural representation steganography scheme that integrates dual-key Fourier encoding, shared-parameter training and dynamic loss weight strategy to embed two images in a single model. A low-frequency-dominated Fourier matrix preserves the cover’s global colour and contour, while a high-frequency-enhanced matrix captures the secret’s fine textures; both representations are concatenated and fed into a five-layer fully connected network with shared weights for joint training. During training, a dynamic loss weight strategy is adopted to adaptively adjust loss weights, guaranteeing the visual naturalness of the cover while emphasising the secret image’s detail recovery. Experiments demonstrate substantial gains in the reconstruction quality of the secret image and embedding capacity compared with conventional methods. In addition, the model-key decoupling paradigm proposed in this paper separates public components (model parameters and public key) from the secret key, which is delivered through an encrypted channel, enabling authorized users to perform offline secret retrieval and significantly reducing interception risk.
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Journal of Cybersecurity. 2026, 4(2): 75-89. https://doi.org/10.20172/j.issn.2097-3136.260406
As an important component of intelligent transportation systems, intelligent vehicular networks (V2X) enable information exchange between smart vehicles and roadside base stations through wireless networks. In high-speed mobile communication scenarios, traditional authentication mechanisms are prone to security attacks and high latency. V2X urgently require secure and reliable authentication mechanisms that can adapt to large-scale real-time communication scenarios. This paper proposes a distributed anonymous authentication and key negotiation protocol for intelligent V2X supporting dual-blockchain collaborative computing. It achieves threshold cooperative authentication among multiple roadside base stations through secret sharing technology, avoiding the risk of single points of failure brought by the authentication mechanism. The protocol utilizes a dual-blockchain hierarchical structure and smart contract-driven approach to achieve high-throughput secure communication. It combines cuckoo filter technology to realize rapid retrieval of public key fingerprints of intelligent vehicles, and achieves anonymous identity traceability and privacy protection of intelligent vehicles. Performance comparisons and analysis demonstrate that the protocol has lightweight advantages in terms of authentication delay, computational overhead, and communication overhead, making it suitable for dynamic, cross-regional distributed intelligent vehicle systems.
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Journal of Cybersecurity. 2026, 4(2): 90-99. https://doi.org/10.20172/j.issn.2097-3136.260407
With the deep integration of generative artificial intelligence into advertising content creation and delivery, it not only improves efficiency and personalization but also introduces new and complex risks of discriminatory content. To address the difficulty in identifying potential discriminatory content in advertising images, this paper proposes a novel deep convolutional neural network architecture, termed ResNet-SPP-ResNet. The model integrates dual residual networks (ResNet) with a spatial pyramid pooling (SPP) mechanism to form a symmetric network structure, thereby enhancing multi-scale feature representation. To support model training and evaluation, we constructed a new advertising image dataset, AD-IMAGE-2024, which consists of real-world advertisement samples collected over the past five years, with annotated discriminatory labels and standardized preprocessing. Comparative experiments conducted on this dataset demonstrate that the proposed model achieves superior performance in binary classification tasks, with an accuracy of
0.8374 , precision of0.8222 , recall of0.8295 , and an F1-score of0.8258 , outperforming mainstream models such as Transformer, Vision Transformer, and DenseNet. This study not only introduces an effective innovation in network architecture design but also provides a “safety auditor” and “quality inspector” for generative AI outputs, helping to mitigate the generation and dissemination of discriminatory content at the source, and providing important support for the safe and reliable application of generative AI in the field of advertising. -
Journal of Cybersecurity. 2026, 4(2): 100-114. https://doi.org/10.20172/j.issn.2097-3136.260408
Cryptographic technology serves as the foundational supporting technology for cyberspace security. The rapid advancement of quantum computing poses a severe security threat to traditional cryptographic systems, making post-quantum cryptography (PQC) migration an effective pathway for safeguarding cyberspace security. As a core enabler of this migration process, conducting research on cryptographic agility technology is of significant practical importance. Against this backdrop, this paper conducts a systematic review of cryptographic agility technology from the perspectives of definitions and connotations, technological breakthroughs, scenario-based applications, and governance policies. It synthesizes the viewpoint that cryptographic agility is not merely algorithm replacement but a security technology at the architectural level; It investigates the security of cryptographic agility, summarizes updatable functional modules based on the universal composability security framework, and refines key agility technologies for cryptographic protocols, software, and hardware; It analyzes its specific applications in domains such as the domain name system, aviation, and industry, and explores relevant strategic frameworks and technology maturity models; It refines the design paradigms for cryptographic agility and provides an outlook on its future research directions to offer theoretical and technological insights for PQC migration.
Volume 4 Issue 2
Bimonthly
Sponsored by China Aerospace Academy of Systems Science and Engineering
ISSN 2097-3136
CN 10-1901/TP
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