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Journal of Information Security Reserach    2025, 11 (E2): 277-.   Abstract （1030）      PDF （1198KB）（16）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E1): 19-.   Abstract （847）      PDF （1799KB）（27）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E2): 80-.   Abstract （811）      PDF （1807KB）（23）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E2): 295-.   Abstract （479）      PDF （893KB）（19）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E2): 197-.   Abstract （334）      PDF （815KB）（20）       Knowledge map    Save Reference | Related Articles | Metrics

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A Survey on Backdoor Attacks and Defenses in Federated Learning Journal of Information Security Reserach    2025, 11 (9): 778-.   Abstract （308）      PDF （2638KB）（85）       Knowledge map    Save Federated learning is a machine learning framework that enables participants in different fields to participate in largescale centralized model training together under the condition of protecting local data privacy. In the context of addressing the pressing issue of data silos, federated learning has rapidly emerged as a research hotspot. However, the heterogeneity of training data among different participants in federated learning also makes it more vulnerable to model robustness attacks from malicious participants, such as backdoor attacks. Backdoor attacks inject backdoors into the global model by submitting malicious model updates. These backdoors can only be triggered by carefully designed inputs and behave normally when input clean data samples, which poses a great threat to the robustness of the model. This paper presents a comprehensive review of the current backdoor attack methods and backdoor defense strategies in federated learning. Firstly, the concept of federated learning, the main types of backdoor attacks and backdoor defenses and their evaluation metrics were introduced. Then, the main backdoor attacks and defenses were analyzed and compared, and their advantages and disadvantages were pointed out. On this basis, we further discusses the challenges of backdoor attacks and backdoor defenses in federated learning, and prospects their research directions in the future. Reference | Related Articles | Metrics

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A Symbioticbased Framework for AI Safety Governance Journal of Information Security Reserach    2025, 11 (10): 897-.   Abstract （285）      PDF （2070KB）（76）       Knowledge map    Save Artificial intelligence technology is currently developing at an unprecedented pace, with safety concerns becoming a global focal point. Traditional AI safety research has predominantly relied on a “control paradigm”, emphasizing limitations, regulations, and value alignment to control AI behavior and prevent potential risks. However, as AI capabilities continue to strengthen, unidirectional control strategies are revealing increasingly significant limitations, with issues such as transparency illusions, adversarial evolution, and innovation suppression gradually emerging. Industry leaders like Sam Altman and Dario Amodei predict that AI may comprehensively surpass human capabilities in multiple fields within the next 23 years, making the reconstruction of AI governance paradigms particularly urgent. This paper proposes a new perspective—the “symbiotic paradigm”—emphasizing humanmachine collaboration as the core and understanding and trust as the foundation. Through establishing four pillars: transparent communication, bidirectional understanding, creative resonance, and dynamic boundaries, it promotes AI safety’s transition from control to cocreation, serving as one of the foundational paths for digital governance transformation. This paper systematically demonstrates the feasibility and necessity of the symbiotic paradigm through four dimensions: theoretical analysis, technological paths, practical cases, and governance recommendations, aiming to provide a sustainable alternative for future AI safety research and digital governance practices. Reference | Related Articles | Metrics

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TCNGANbased Temporal Traffic Anomaly Detection Journal of Information Security Reserach    2025, 11 (10): 907-.   Abstract （241）      PDF （2708KB）（58）       Knowledge map    Save In recent years, generative adversarial networks have been widely used in the field of temporal anomaly detection. However, temporal data often has complex timedependence, and problems such as gradient vanishing and training instability are common in existing anomaly detection models. To this end, this paper proposes an unsupervised temporal traffic anomaly detection model based on the combination of temporal convolutional network (TCN) and GAN. The model uses TCN as the infrastructure of generator and discriminator, which can effectively capture the temporal features of the temporal traffic data. During the anomaly detection process, the model constructs an anomaly scoring function based on the reconstruction loss and discriminator loss, and performs anomaly judgment by setting a threshold, thus improving the accuracy of anomaly detection. To verify the performance of the proposed model, experiments are conducted on five different types of datasets. The results show that the average F1 score of the proposed model is 11.02% higher than that of the TAnoGAN model. Reference | Related Articles | Metrics

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Research on Network Unknown Attack Detection Based on Machine Learning#br# #br# Journal of Information Security Reserach    2025, 11 (9): 807-.   Abstract （227）      PDF （1297KB）（54）       Knowledge map    Save In the complex context of the continuous evolution of cybersecurity threats, the threats posed by unknown network attacks to digital infrastructure are increasing daily. Consequently, The technology for detecting unknown network attacks based on machine learning has emerged as a focal point in research. This paper first discusses the classification of intrusion detection systems and the commonly used technologies for detecting unknown network attacks. Subsequently, it conducts an indepth exploration of the methods for detecting unknown attacks based on machine learning from three dimensions: anomaly detection, openset recognition, and zeroshot learning. Furthermore, it summarizes the commonly used datasets and key evaluation indicators. Finally, it summarizes and looks ahead to the development trends and challenges of unknown attack detection. This article can provide references for further exploring new methods and technologies in the field of cyberspace security. Reference | Related Articles | Metrics

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Research on the Legal Positioning and Liability Allocation of AI Agent Journal of Information Security Reserach    2026, 12 (8): 681-690.   DOI: 10.12379/j.issn.2096-1057.2026.08.01 Abstract （222）      PDF （682KB）（11）       Knowledge map    Save The autonomous operation and continuous functioning capabilities of AI agents have transcended the operational boundaries of traditional generative AI, which centers on the "input-output" paradigm, and present new normative challenges to the existing "tool-control-responsibility" framework a framework predicated on stable human control. In terms of behavior identification, responsibility attribution, and operational regulation, the current legal system is insufficient to fully accommodate the cross-subject, multi interactive, and continuously operating characteristics of AI agents. Against this backdrop, this paper contends that AI agents should not be granted independent legal personality. Instead, it proposes a response grounded in the functional reconstruction of traditional static tool-oriented rules, while preserving the stability of the existing subject system. Adopting "functional instrumentalism" as the fundamental legal orientation for AI agents, this paper constructs a dual track identification framework "technological identity" and "legal attribution" to align behavioral identifiability with responsibility attributability under conditions of continuous operation. Furthermore, it introduces a "relationalist attribution" approach, which takes control relationships, interest structures, and risk sources as analytical dimensions to allocate responsibilities among multiple subjects in a structured manner. Employing the "principle of minimum necessity" as the boundary for institutional expansion, the paper thereby outlines an integrated governance framework that connects current law, special rules, and technical standards. Accordingly, the logic of AI governance is shifting from a static structure centered on behavioral outcomes and one off liability determinations toward a dynamic structure oriented around process of continuous operation. Reference | Related Articles | Metrics

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Research on Critical Information Infrastructure Security Protection Journal of Information Security Reserach    2025, 11 (10): 878-.   Abstract （219）      PDF （324KB）（77）       Knowledge map    Save Related Articles | Metrics

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Research on Security Assurance of Egovernment Journal of Information Security Reserach    2025, 11 (10): 879-.   Abstract （203）      PDF （865KB）（60）       Knowledge map    Save government encompasses critical domains including government operations, public services, and data management, and its security directly affects national interests, public wellbeing, and social stability. In recent years, cyberattacks targeting Egovernment systems have become more frequent and continue to rise, security risks of government administrative networks continued to mount up and challenge security protection. This paper analyzes the development paths of Egovernment security protection at home and abroad and proposes relevant policy recommendations, with the aim of providing strong support for building a more perfect and optimized Egovernment security protection system. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2026, 12 (2): 98-.   Abstract （202）      PDF （532KB）（134）       Knowledge map    Save Related Articles | Metrics

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The Enlightenment and Reference of Cybersecurity Protection Policies for  Critical Information Infrastructure Journal of Information Security Reserach    2025, 11 (10): 885-.   Abstract （199）      PDF （920KB）（37）       Knowledge map    Save The security and stability of critical information infrastructure (CII) are of crucial importance to national security, economic development, and social stability. The insights and lessons learned from the CII security safeguards policies of countries and organizations such as the European Union, Japan, the United States, and Russia merit reference. CII security safeguards policies in China has gone through the stages of early exploration, rapid development, and comprehensive advancement; it is confronted with real predicaments including insufficient policy foresight, inadequate crossdomain coordination and collaboration, poor coordination and alignment of standards, and weak discourse power in international rules. It is suggested that China should strengthen the strategic guidance and toplevel design for CII, improve the crossdomain overall planning and linkage mechanism, formulate and refine CII protection standards. Reference | Related Articles | Metrics

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Research on Traffic Anomaly Detection Method and System for API Gateway Journal of Information Security Reserach    2025, 11 (10): 917-.   Abstract （199）      PDF （1061KB）（42）       Knowledge map    Save With the rise of cloud services and the widespread use of API technology, many network capabilities of operators are usually outputted and empowered through APIs. API gateways have become an important way for northsouth and eastwest system interconnection and data sharing. This paper proposes a method for API gateway traffic anomaly detection based deep learning. Firstly, a heterogeneous graph is constructed to comprehensively represent the gateway traffic network. Then, based on graph attention neural network, node representations in the heterogeneous graph are learned by considering both structural and temporal dimensions. We introduce graph structure refinement to compensate for sparse connections between entities in the heterogeneous graph and obtain more robust node representation learning; Finally, the meta learning algorithm is used to optimize the model and improve its generalization ability in small sample scenarios. The model can be deployed on gateway devices. The algorithm model was experimentally evaluated on the CICIDS2017 dataset, and the results showed that compared with the baseline algorithm, the detection method proposed in this paper has good performance in small sample and multi classification problems. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E1): 9-.   Abstract （198）      PDF （1462KB）（20）       Knowledge map    Save Reference | Related Articles | Metrics

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A Deep Learning Differential Privacy Protection Scheme Based on  Adaptive Clipping Journal of Information Security Reserach    2026, 12 (6): 490-.   Abstract （192）      PDF （1728KB）（108）       Knowledge map    Save To address the issues of utility degradation in deep learning models under differential privacy protection and the gap between theoretical and actual privacy protection effectiveness, this paper proposes a deep learning differential privacy protection scheme based on adaptive clipping. The scheme optimizes the process through a fourstep mechanism: firstly, gradient adaptive clipping controls the gradient magnitude during training by dynamically adjusting the gradient clipping threshold, thereby enabling the control of the magnitude of noise added subsequently; secondly, group label selection identifies the group with the smallest gradient as the privacypreserving object, and more accurate privacy loss can be obtained by training this group; thirdly, optimized privacy loss calculation combines the gaussian mechanism based on subsampling to reduce the computational overhead of model privacy loss calculation; finally, optimized gradient adaptive descent realizes the adaptive descent of gradients by adjusting the conditional smoothing parameter, thus improving the usability of the model. Experiments were conducted on the VGG architecture using the MNIST, CIFAR10, and MedicalMNIST datasets. The results show that the model accuracy rates after training with this scheme are 81.08%, 72.30%, and 67.91% respectively, representing improvements of 15.60%, 10.60%, and 9.71% compared to the traditional DPSGD, and 0.63%, 2.50%, and 4.40% over the widely used Nadam algorithm in recent years. The model training efficiency has been improved by 35.5% and 39.4%, respectively. Reference | Related Articles | Metrics

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Research on Frontier Technologies for Critical Information  Infrastructure Security Protection Journal of Information Security Reserach    2025, 11 (12): 1075-.   Abstract （191）      PDF （994KB）（51）       Knowledge map    Save Currently, China’s critical information infrastructure (CII) faces significant threats, including statesponsored cyber attacks and supply chain disruptions. This research aims to systematically analyze the key technological frameworks and development trends in CII security protection, assess China’s current technological capabilities and core bottlenecks in this domain, and propose development strategies and implementation pathways aligned with national conditions. Focusing on key technology clusters such as dynamic active defense, intelligent analysis and response, and resilience architectures, the study explores their synergistic application mechanisms and integration points with existing policies. The study seeks to provide critical technical support and policy recommendations for enhancing the security resilience and compliance of CII. Reference | Related Articles | Metrics

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Internet of Things Intrusion Detection Model Based on Federated Learning Journal of Information Security Reserach    2025, 11 (9): 788-.   Abstract （174）      PDF （1432KB）（41）       Knowledge map    Save The Internet of things (IoT) has shown a wide range of application prospects and huge development potential in many fields. However, as the scale of the IoT continues to expand, independent IoT devices lack highquality attack instances, making it difficult to effectively respond to increasingly complex and diverse attack behaviors. Consequently, addressing IoT security issues has become a critical challenge that requires urgent attention. To address this problem, the paper proposes an IoT intrusion detection model based on federated learning and attention mechanisms, which allows multiple devices to train the global model collaboratively while protecting their data privacy. Firstly, this paper constructs an intrusion detection model combining convolutional neural network and mixed attention mechanism to extract key features of network traffic data, so as to improve detection accuracy. Secondly, the paper introduces the model contrast loss to correct the training direction of the local model to alleviate the global model convergence difficulties caused by the nonindependent and same distribution of data between devices. The experimental results show that the proposed model is significantly superior to the existing methods in terms of accuracy, accuracy and recall, demonstrating stronger intrusion detection capabilities, and can effectively deal with complex data distribution problems in the IoT environment. Reference | Related Articles | Metrics

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Compound Admissibility Rules of Blockchain Evidence in Online Litigation Journal of Information Security Reserach    2026, 12 (2): 134-.   Abstract （172）      PDF （1088KB）（48）       Knowledge map    Save Blockchain evidence offers a solution to the limitations of traditional electronic evidence by establishing a new model of “evidence selfauthentication”. However, current regulations in China exhibit obvious limitations, failing to fully cover the application of blockchain evidence in both online and offline spaces, while prioritizing authenticity at the expense of admissibility. To realize the proper application of blockchain evidence in the Chinese context, this paper proposes a dualspace framework integrating technological selfauthentication with legal presumptions. This approach aims to achieve consensual justice, composite admissibility rules for preservation, presentation, crossexamination, and authentication, and thereby foster a novel form of evidence rule of law with benign interaction between rule of law and technical rule of law. Reference | Related Articles | Metrics

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Design of Intrusion Detection System for Oil and Gas Production IoT #br# Based on Edgecloud Collaboration#br# Journal of Information Security Reserach    2025, 11 (9): 868-.   Abstract （169）      PDF （2738KB）（22）       Knowledge map    Save Aiming at the multifaceted intrusion threats in the oil and gas production IoT, this paper proposes an intrusion detection system based on edgecloud collaboration. The system is designed to meet the high requirements for realtime performance and accuracy, while overcoming challenges such as limited edge computing resources and data heterogeneity between edge and cloud environments. The system adopts a cloudedge collaborative architecture, with different intrusion detection subsystems deployed at the edgecloud, working in coordination to ensure comprehensive protection. The edge uses a model based on independent classification and joint analysis to accurately detect anomalies in multiple physical data, achieving detection speeds within 100 milliseconds. The cloud uses a model based on feature extraction + XGBoost, and adopts pretraining and finetuning to obtain a detection model with both anomaly traffic detection capability and low false alarm rate. The simulation results show that the system achieves high accuracy and realtime performance, adapts to the differences in available computing resources of the edge and cloud devices, and satisfies the performance requirements of intrusion detection across different levels. Reference | Related Articles | Metrics

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Imperceptible Proactive Defense Method Against Face Attribute Editing Journal of Information Security Reserach    2025, 11 (10): 941-.   Abstract （168）      PDF （2504KB）（26）       Knowledge map    Save Although the face attribute editing forgery active defense method based on generative adversarial network (GAN) generates adversarial perturbations faster than the gradient attackbased methods, existing methods still fail in balancing the proactive defense effect with the imperceptibility of generated perturbations. Therefore, this paper proposed a highly imperceptible proactive defense method against face attribute editing based on GAN. To enhance the imperceptibility of the perturbations, the method designed a highfrequency information compensation mechanism to enable the generator to generate more highfrequency perturbations that are less sensitive to the human eye. To improve the proactive defense performance of generated perturbations, the proposed method also designed a multilevel dense connection mechanism for reducing semantic loss during the encoding process. Meanwhile, the method introduced face saliency adversarial loss in training stage to enable perturbations to disrupt face forgery areas better. The experiments were conducted in both singlemodel and crossmodel defense scenarios. The results indicate that compared to existing methods, the proposed method generates more imperceptible adversarial perturbations and obtains high success rates for defending against target models. Related Articles | Metrics

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Government Data Catalog Security Sharing Model Based on Editable Blockchain Journal of Information Security Reserach    2025, 11 (10): 966-.   Abstract （167）      PDF （6159KB）（74）       Knowledge map    Save As government demand for data sharing rises, ensuring data security and reliability has become critical. This paper proposes a secure sharing model for government data catalogs using editable blockchain, which facilitates collaborative updates both onchain and offchain, incorporates finegrained editing permissions, and implements robust security controls. The model employs a dualtrapdoor chameleon hash function with a temporary trapdoor key for onchain updates, addressing the problem that traditional key splitting and recovery schemes cannot balance security and efficiency. Additionally, it introduces an editing permission authorization mechanism that combines user IDbased multiinstitution attribute encryption with temporary trapdoor keys, ensuring accurate permission management across departments. A thorough security analysis confirms the model’s effectiveness in mitigating various security threats. The analysis reveals that the proposed model significantly enhances the trustworthiness of government data sharing by effectively addressing security challenges and ensuring data integrity. These findings highlight the potential of editable blockchain technology in transforming how government entities manage and share sensitive information. Reference | Related Articles | Metrics

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DGA Domain Name Generation Method of BiLSTM Model  Based on Bayesian HPO Journal of Information Security Reserach    2025, 11 (10): 950-.   Abstract （166）      PDF （1488KB）（22）       Knowledge map    Save In recent years, domain generation algorithms (DGA) have been extensively utilized in network attacks to dynamically generate large quantities of random domain names for malicious software communications, posing a severe challenge for security defenses. As DGA structures grow increasingly complex, traditional domain classification methods that rely on manually extracted features struggle to adapt to new variants in a timely manner. Although generationbased deep models can automatically capture latent patterns from data, their large parameter sizes and intricate hyperparameter tuning often hinder stable performance across diverse DGA. To tackle these issues, this paper proposes a DGA domain generation approach based on a bidirectional long shortterm memory (BiLSTM) model enhanced by Bayesian hyperparameter optimization(Bayesian HPO). By automating the tuning of critical hyperparameter, our method significantly reduces manual intervention and training overhead, while strengthening the robustness and generalization capability of the model against various DGA. Experimental results demonstrate that the proposed approach achieves excellent generation accuracy on multiple DGA families, providing a proactive, forwardlooking defense strategy for network security. Reference | Related Articles | Metrics

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Robust Malicious Encrypted Traffic Detection Method Based on  Dual Confidence Sample Selection Journal of Information Security Reserach    2025, 11 (10): 924-.   Abstract （165）      PDF （1679KB）（25）       Knowledge map    Save In the task of detecting malicious encrypted traffic, the existence of noise tags seriously affects the generalization ability and detection accuracy of the model. To solve the above problems, a noise label learning method based on DCASS (dualconfidence adaptive sample selection) is proposed to realize robust malicious encryption traffic detection. Firstly, the low dimensional features of samples are extracted by self encoder, and the feature confidence of samples is constructed.Then, the label confidence of samples is evaluated according to their performance in classification training. Finally, an adaptive selection threshold is proposed to select samples based on the dual confidence of feature space and label space, and filter noise samples dynamically to improve the robustness of the model. Experiments on CIRACICDoHBrw2020 dataset show that the proposed method has good performance and stability in dealing with noise labels. The F1 scores of the method reach 86.686%, 86.749%, 83.199% respectively when the noise rate is 20%, 30%, 40%. Compared with the existing three methods, the method proposed in this paper shows the best performance under different noise rates, with the average performance improvement of 18.89%, 37.34%, 6.32% respectively. Reference | Related Articles | Metrics

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Research on the Governance System of Ensuring Both Crossborder #br# Data Flow and Safety#br# #br# Journal of Information Security Reserach    2025, 11 (9): 840-.   Abstract （164）      PDF （1493KB）（29）       Knowledge map    Save Crossborder data flow is a fundamental part of digital trade, and it is also a key issue in the international data governance game. At present, China has preliminarily formed a system of rules and regulations for crossborder data flow, but there are still prominent problems such as difficulties in crossborder risk screening, slightly rough rules and systems, difficulties in the dominance of international rules, and weak crossborder regulatory means. To seriously  solve the problem of crossborder data flow, it is urgent to better coordinate development, security and openness to build a crossborder data governance system. The policy system clarifies basic propositions, improves the legal system, refines institutional rules, consolidates technology platforms and expands practice carriers, in order to align with highstandard international economic and trade rules, and gradually form a plan for crossborder data flow with Chinese characteristics. Reference | Related Articles | Metrics

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Research on Domain Adaptive Intrusion Detection Method Based on  Dynamic Feature Fusion Journal of Information Security Reserach    2026, 12 (4): 294-.   Abstract （163）      PDF （1452KB）（107）       Knowledge map    Save Aiming at the problems of incomplete feature extraction and limited model generalization ability in intrusion detection research, a domain adaptive intrusion detection method with dynamic feature fusion is proposed. Firstly, a convolutional neural network is used to extract spatial features, while a bidirectional long shortterm memory network is utilized for temporal feature extraction. This approach enables comprehensive extraction of multidimensional feature information from network traffic data. Secondly, the uncertainty is measured by calculating the information entropy of the two features, and different weights are assigned according to the entropy value, and the extracted features are weighted and fused according to the weights. Finally, during the training process, the proposed adaptive domain weight loss algorithm is used to dynamically adjust the contribution of the source domain and target domain data to improve the generalization ability of the model on the target domain data. Experiments are carried out using the NSLKDD and UNSWNB15 datasets. Compared with the existing mainstream methods, this method has higher detection accuracy, which is 0.8563 and 0.916 respectively. Reference | Related Articles | Metrics

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SM9based Decentration Crosschain Medical Data Sharing Scheme Yu Huifang and Li Shunkai Journal of Information Security Reserach    2025, 11 (9): 832-.   Abstract （162）      PDF （2204KB）（65）       Knowledge map    Save To solve the problems of data leakage and data silos between medical institutions in medical system, a SM9based decentration crosschain medical data sharing scheme (DCCMDSS) is proposed in this article. Relay chain and hash time lock contract (HTLC) realize the crosschain data sharing between medical institutions, the interplanetary file system (IPFS) reduces the storage pressure of blockchain and ensures the integrity of medical data. SM9based algorithm encrypts medical data and group signature allows the group members to sign the data on behalf of the whole group without revealing their personal identities. Consequently, DCCMDSS effectively avoids the privacy leakage and ensures the traceability of signature. DCCMDSS reduces the crosschain transaction overhead and improves the security of medical data. Reference | Related Articles | Metrics

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A Review of Large Language Model-Driven Network Penetration Testing Agents Journal of Information Security Reserach    2026, 12 (8): 691-711.   DOI: 10.12379/j.issn.2096-1057.2026.08.02 Abstract （154）      PDF （3915KB）（10）       Knowledge map    Save With the accelerated development of artificial intelligence, intelligent agents have demonstrated notable advantages in environmental perception, task planning, and multi-tool coordination. Concurrently, breakthroughs in Large Language Models concerning natural language understanding, logical reasoning, and multimodal processing have provided crucial support for the evolution of intelligent agents. The deep integration of these two technological strands has given rise to LLM-driven autonomous agents for network penetration testing, promoting a gradual shift from the traditional "tool-assisted" paradigm toward "autonomous intelligence." This paper systematically reviews the key challenges and principal technical approaches identified in existing research across four core modules: agent role definition, task planning, memory management, and interactive execution. It further examines the limitations of current methods in areas such as multimodal information processing, automated interaction, and context management. To address these issues, and in view of the ongoing technological evolution of intelligent agents, this paper proposes several promising research directions for intelligent penetration testing. These include multimodal fusion mechanisms, collaborative strategies integrating memory enhancement with reinforcement learning, and knowledge-graph-based vulnerability discovery methods. The analysis indicates that LLM-driven agents for network penetration testing provide substantial technical support for advancing the intelligence and autonomy of cybersecurity operations. Reference | Related Articles | Metrics

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Research on Lightweight Implicit Certificate Scheme for #br# Resourceconstrained Devices in Distribution Networks#br# #br# Journal of Information Security Reserach    2025, 11 (9): 845-.   Abstract （151）      PDF （1576KB）（19）       Knowledge map    Save As resourceconstrained terminal devices such as fault indicators and smart meters are increasingly deployed in power distribution networks, the security requirements for identity authentication systems have also intensified. However, existing regulations remain inadequate, and traditional public key infrastructure (PKI) technologies are difficult to apply directly due to its heavy burden. To address this issue, this paper proposes a lightweight implicit certificate scheme, improving the elliptic curve QuVanstone (ECQV) implicit certificate algorithm tailored for resourceconstrained environments. The scheme incorporates certificate field optimization and the concise binary object representation (CBOR) encoding, significantly reducing the storage and computational overhead for devices while enhancing system security. Through several simulation analyses under the computer platform, comparing the ECQV implicit certificate scheme before improvement with the traditional X.509 authentication scheme, the results show that the performance of this scheme is more superior. Through experimental verification, the proposed scheme is able to meet the multiple needs of authentication of resourceconstrained devices in the power distribution network, such as storage, computing, energy consumption, and so on. Reference | Related Articles | Metrics

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Research on Highquality Development of New Infrastructures Under  Critical Information Infrastructure Security Protection Journal of Information Security Reserach    2025, 11 (10): 891-.   Abstract （150）      PDF （957KB）（31）       Knowledge map    Save Developing new infrastructure plays a crucial role in enhancing the security protection capabilities of critical information infrastructure. The approaches adopted by relevant countries in advancing new infrastructure—such as boosting global competitiveness, prioritizing key technology R&D, attracting deep private sector participation, promoting unified standards and regulations, and strengthening supply chain resilience—offer valuable insights. Although China’s new infrastructure has seen continuous improvements in recent years regarding development scale, technological autonomy, digital and intelligent capabilities, and its capacity to support critical infrastructure, it also faces challenges such as significant intrinsic security risks, risks associated with introducing new technologies, and lagging standardization efforts. It is recommended in terms of to drive the highquality development of new infrastructure by leveraging intelligent upgrades as the driving force, functional expansion as the connecting link, and boundary governance as the focal point. Reference | Related Articles | Metrics

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Fileless Obfuscation Attack Recognition Based on Semantic Recovery and  Large Language Model Journal of Information Security Reserach    2025, 11 (12): 1125-.   Abstract （149）      PDF （1478KB）（19）       Knowledge map    Save With the continuous advancement of fileless attack techniques and strategies, research on identifying fileless malicious attack has garnered significant attention. Among these, fileless obfuscation attack, as a new type of covert, dynamic, and complex attack, can rapidly bypass existing attack engines and rulebased frameworks. To address this problem, this paper proposes an attack script restoration method guided by dynamic partial execution and semantic analysis tree guidance, enabling the restoration of obfuscated code. Furthermore, leveraging the efficiency of large models in attack understanding and semantic recognition, we integrate large models to achieve efficient identification and classification of fileless code. To further alleviate the limitations of large models in handling large code files and long passages, we also provide a semantic code compression strategy to retain critical attack semantics. Experimental results demonstrate that our proposed semantic restoration and large model identification methods can enhance effectiveness by around 10% compared to existing models and methods, while maintaining efficient attack identification efficiency. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E1): 102-.   Abstract （147）      PDF （997KB）（36）       Knowledge map    Save Reference | Related Articles | Metrics

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A Spectre Vulnerability Detection Method Integrating Fuzzing and #br# Taint Analysis#br# Journal of Information Security Reserach    2025, 11 (9): 822-.   Abstract （146）      PDF （1848KB）（42）       Knowledge map    Save Aiming at the problems of insufficient applicability of traditional vulnerability detection technology in Spectre V1 vulnerability detection, high false positive rate and false positive rate, a novel method TransFT integrating fuzz testing and taint analysis is proposed. First, program code is refactored to simulate the misprediction behavior of Spectre V1 vulnerabilities. Next, feedbackdriven fuzz testing is utilized to identify highrisk code segments and generate test cases capable of triggering vulnerabilities, thereby improving testing efficiency. Finally, static taint analysis is applied to validate potential vulnerabilities, effectively reducing FNR and FPR. Experimental results demonstrate that the proposed method significantly reduces FNR, FPR, and testing time compared to existing fuzzingbased approaches, showcasing superior detection capabilities. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E1): 14-.   Abstract （144）      PDF （999KB）（41）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Multimodal Cyberspace Identification Technology  Based on Object Identifier Journal of Information Security Reserach    2025, 11 (10): 960-.   Abstract （143）      PDF （1253KB）（24）       Knowledge map    Save Multimodal cyberspace identification is a basic work for the construction of multimodal cyberspace. This paper summarizes the current state of identification system research both domestically and internationally, and provides a comparative analysis of various identification technologies. In view of the large number of communication devices in multimodal cyberspace and the high requirements of endogenous security, a multimodal cyberspace identification technology based on object identifiers is proposed, and the coding rules of tree structure are used to identify and manage largescale communication devices in multimodal cyberspace to improve management efficiency. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E2): 89-.   Abstract （143）      PDF （1508KB）（71）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Security Challenges and Countermeasures for Critical  Information Infrastructure in the Artificial Intelligence Era Journal of Information Security Reserach    2025, 11 (12): 1087-.   Abstract （141）      PDF （944KB）（45）       Knowledge map    Save With the rapid advancement of artificial intelligence (AI) technologies, critical information infrastructure is confronting unprecedented security challenges. This paper employs systematic analysis and comparative research methods to examine the security threats faced by critical information infrastructure in the AI era, specifically focusing on structural vulnerabilities, governance lag, and dual technical risks. Drawing on the strategic practices of major economies such as the United States, the European Union, and Japan, it proposes that China should enhance AI security policy standards, establish a security risk governance framework, and strengthen security technology innovation. Through these pathways, China can build a selfreliant, secure, and reliable AIenabled critical information infrastructure system, thereby enhancing national digital security capabilities and global competitiveness. Reference | Related Articles | Metrics

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Survey of Software Supply Chain Security Detection and Assessment Technologies Journal of Information Security Reserach    2026, 12 (7): 586-597.   Abstract （140）      PDF （1750KB）（78）       Knowledge map    Save In the context of the digital era, software supply chain has become a critical component supporting the stable and healthy development of the digital economy. It is an indispensable part of the nation’s key information infrastructure and economic and social systems. The security of software supply chain directly determines the security of the key businesses carried by the software supply chain. Therefore, based on the development needs of the digital age, this article summarizes the current technologies, methods, and development trends related to software supply chain security detection and evaluation, providing reference and guidance for industry insiders, researchers, and decisionmakers. It includes a review and detailed explanation of the background and methods of software supply chain security detection and evaluation technology, detailing the principles of mainstream technologies such as component analysis, vulnerability scanning, code review, runtime monitoring, threat modeling, and fuzz testing, and comparing and analyzing the advantages and disadvantages of various technologies; Analyze the current technical challenges and countermeasures faced by technology; And propose ten major trends for the development of this field in the next decade, in order to improve the security level of the software supply chain and promote the development of the software industry. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E1): 5-.   Abstract （139）      PDF （1368KB）（35）       Knowledge map    Save Reference | Related Articles | Metrics