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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）（77）       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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Overview on SM9 Identity Based Cryptographic Algorithm Journal of Information Security Research    2016, 2 (11): 1008-1027.   Abstract （3789）      PDF （13949KB）（6269）       Knowledge map    Save SM9 identitybased cryptographic algorithm is an identitybased cryptosystem with bilinear pairings. In such a system the user s private key and public key may be extracted from user s identity and key generation centers parameters. The most common cryptographic uses of SM9 are with digital signature, data encryption, key exchange protocol and key encapsulation mechanism etc. The application and management of SM9 will not require digital certificate, certificate base, and key base. The key length of the SM9 cipher algorithm is 256b. SM9 cryptographic algorithm was issued as the cryptography standard in 2015. This paper will summarize the design, algorithm, software and hardware implementation and cryptanalysis of SM9 cryptographic algorithm. We also give some concrete examples in appendix. Reference | Related Articles | Metrics

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Research on Data Classification and Grading Method Based on Data Security Law Journal of Information Security Reserach    2021, 7 (10): 933-.   Abstract （1744）      PDF （2157KB）（1133）       Knowledge map    Save The Data Security Law of the People's Republic of China (hereinafter referred to as the Data Security Law) has been formally promulgated, which clearly stipulates that the state establishes data classification and grading protection system, and implements classified and graded protection for data. However, at present, the relevant standards and specifications of data classification and grading in China are relatively lacking, and the practical experiences that can be used for reference in various industries are relatively insufficient. How to effectively implement the data classification and grading protection is still a thorny problem. Based on Article 21 of the Data Security Law, this paper analyzes the factors such as the influence object, influence breadth and influence depth after the data is damaged, puts forward the principles and methods of data classification and data grading, and gives an implementation path of data classification and grading according to the application scenarios and industry characteristics of the data, which provide a certain reference for data classification and grading protection of various industries. Reference | Related Articles | Metrics

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The Structural Risk Evolution of Virtual Currency Crimes and Chinese Governance Practices Journal of Information Security Reserach    2026, 12 (9): 860-866.   DOI: 10.12379/j.issn.2096-1057.2026.09.09 Abstract （24）      PDF （683KB）（21）       Knowledge map    Save With the deep development of blockchain technology and the accelerated integration of the global digital economy, virtual currencies are reshaping the financial ecosystem, but simultaneously giving rise to new types of criminal activities with diverse forms and far-reaching harms. These activities exhibit deep-seated characteristics such as disembedded subject identities, modularized criminal activities, technological iteration of criminal methods, and networked criminal hazards, becoming new structural risk carriers in the financial sector. Based on the triple analytical framework of “technology-institution-capital,” this paper systematically analyzes the evolutionary path and deep-seated generation logic of virtual currency crimes from instrumental application to ecological infiltration. On this basis, transcending simple policy reviews, this paper theoretically refines Chinese practical experience in governing new types of virtual currency crimes. The research shows that China has constructed a comprehensive and penetrative governance system through the reshaping of the institutional dimension, the countermeasures in the technological dimension, and the regulation of the capital dimension. This practice not only effectively curbs local risks but also contributes Chinese wisdom with theoretical depth and practical value to risk governance and order reconstruction in the era of global digital finance. Reference | Related Articles | Metrics

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The Mechanism of Disinformation Generation and Governance Pathways in Generative AI Models Journal of Information Security Reserach    2026, 12 (7): 606-612.   Abstract （117）      PDF （1256KB）（38）       Knowledge map    Save While driving transformations in online information order, generative AI models also generate disinformation risks characterized by an “objective+subjective” overlap. An analytical framework tailored to their technical characteristics is urgently needed. This study systematically deconstructs the technical logic of false information production in generative AI models based on their hybrid expert architecture, treelike reasoning patterns, localized semantic understanding attributes, and opensource ecosystem mechanisms. It analyzes the transmission mechanisms of false information risks across four stages: data input, algorithmic operation, content presentation, and cognitive dissemination. To address these risks: At the algorithmic level, implement a processbased oversight scheme encompassing “access reviewoperation disclosurepostevent verification”; at the presentation level, strengthen scenariobased, interactive “warning notice” labeling mechanisms; at the cognitive level, cultivate users' digital literacy and selfrestraintt capabilities to achieve effective information security governance in the AI era. Reference | Related Articles | Metrics

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VEDA, Establishing the AI Dynamic Defense System for Cyber Security Journal of Information Security Research    2017, 3 (12): 1058-1066.   Abstract （447）      PDF （1526KB）（989）       Knowledge map    Save Related Articles | Metrics

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A Survey of Zero Trust Research Journal of Information Security Research    2020, 6 (7): 608-614.   Abstract （1529）      PDF （2068KB）（1701）       Knowledge map    Save With the popularization of cloud computing, mobile office and other technologies, the enterprise network structure becomes complex. The traditional network security model is based on the idea of boundary protection, which can not meet the current needs. Zero trust is a new network security model, where no distinction is made between internal and external networks and all entities need authentication and authorization before accessing resources, which can be used to protect the network whose perimeter is increasingly fuzzy. This paper gives the definition of zero trust, introduces the architecture of zero trust, analyzes the core technology of zero trust, compares and analyses several representative zero trust schemes, summarizes the development status, points out the research direction needing attention in this field, which can provide reference for the research and application of zero trust. 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 （190）      PDF （1728KB）（107）       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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A Deep Learningbased Method for Background Traffic Generation in Railway Cyber Range Journal of Information Security Reserach    2026, 12 (7): 613-624.   Abstract （67）      PDF （2996KB）（25）       Knowledge map    Save Cybersecurity threats have permeated all aspects of the railway industry, necessitating that railway cybersecurity ranges keep pace with the latest security demands. Background traffic is a core element in constructing a realistic simulation environment for cyber ranges and serves as a critical technology supporting range operations. However, effectively capturing the complex spatiotemporal characteristics of network traffic and generating highfidelity background traffic remains a significant challenge. This paper proposes a novel method for generating background traffic in railway cyber ranges (referred to as B2Diff). By integrating deep learning techniques such as BERT, bidirectional long shortterm memory networks, and diffusion models, the method captures features such as contextual semantics, spatiotemporal dependencies, and complex traffic distributions in traffic samples, thereby generating highly realistic and diverse background traffic. Experimental results demonstrate that B2Diff significantly outperforms existing methods in terms of the quality and diversity of the generated background traffic, validating its effectiveness in enhancing the simulation realism of cyber ranges. Reference | Related Articles | Metrics

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Survey of Hash Functions Wang Xiaoyun1,2 and Yu Hongbo3 Journal of Information Security Research    2015, 1 (1): 19-30.   Abstract （1835）      PDF （11279KB）（3929）       Knowledge map    Save One of the fundamental primitives in modern cryptography is the cryptographic hash functions, often informally called hash functions. They are used to compress messages of arbitrary length to fixed length hash values which are also called hash codes, message digests or digital fingerprints. A primary motivation for cryptographic hash functions is that they serve as compact representative images of input messages, which they can uniquely identify. Changing a single letter will change most of the digits in the hash code. The most common cryptographic uses of hash functions are with digital signature and for data integrity. Hash functions are frequently used in digital signature schemes to compress large messages for processing by public-key cryptosystems such as RSA. They are also used to design message authentication codes (MACs) and many secure cryptographic protocols. Hash functions occur as components in various cryptographic applications (e.g. protection of pass-phrases, protocols for payment, broadcast authentication etc.), where usually their property as a computational one-way function is used. So the study of the hash functions is of great significance in the cryptanalysis field. Related Articles | Metrics

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A Survey of Research on Network Attack Model Journal of Information Security Research    2020, 6 (12): 1058-1067.   Abstract （1466）      PDF （1774KB）（1209）       Knowledge map    Save With the rapid development of information technology, network attacks have gradually presented multi-stage, distributed and intelligent characteristics. Single firewalls, intrusion detection systems and other traditional network defense measures cannot well protect the network system security in an open environment. As a kind of attack scene representation from the attacker's perspective, the network attack model can comprehensively describe the network attack behavior in a complex and changeable environment, and is one of the commonly used network attack analysis and response tools. This paper first introduces the current main network attack models, including traditional trees, graphs, nets structure models and modern attack chains, ATT&CK, diamond models, etc. Then the analysis and application of network attack model will be explained. The analysis process for the purpose of solving the attack index mainly includes the probability framework, the assignment method and the solution method, and the application of the attack model based on the life cycle includes the application of the attackers and the defenders' perspective; Finally, the current challenges and future directions of the network attack model and its analysis and application are summarized. Reference | Related Articles | Metrics

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A Survey of Forensic Network Attack Source Traceback Journal of Information Security Reserach    2024, 10 (4): 302-.   Abstract （393）      PDF （1134KB）（261）       Knowledge map    Save The concealment and anonymity of cyber attackers pose significant challenges to the field of network attack traceback. This study provides a comprehensive overview of the current state of research on network attack traceback analysis techniques, focusing on three aspects: traffic, scenarios, and samples. Firstly, with respect to traffic traceback, the paper outlines methods and applications based on log records, packet marking, ICMP tracing, and link testing. Secondly, it categorizes traceback techniques for different scenarios, encompassinganonymous networks, zombie networks, springboards, local area networks, and advanced persistent threat attacks, as well as their applications and limitations in realworld environments. Finally, concerning sample analysis, the paper discusses the progress and application scenarios of static and dynamic traceback analysis in the context of malicious code analysis and attack tracing. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E2): 2-.   Abstract （78）      PDF （1416KB）（55）       Knowledge map    Save Reference | Related Articles | Metrics

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Guiyang Taking Actualization to Ensure the Security of Big Data Journal of Information Security Research    2018, 4 (5): 394-401.   Abstract （324）      PDF （1599KB）（589）       Knowledge map    Save Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 105-.   Abstract （798）      PDF （929KB）（406）       Knowledge map    Save Reference | Related Articles | Metrics

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Interactive Dynamic Privacy Risk Assessment and Adaptive Protection Methods in Data Publishing Journal of Information Security Reserach    2026, 12 (7): 598-605.   Abstract （73）      PDF （1786KB）（23）       Knowledge map    Save Data publishing is an important way to promote data sharing. However, studies have shown that it is accompanied by the risk of privacy leakage of sensitive attribute information due to large and frequent access to data by malicious parties. Although existing evaluation methods consider the scenarios of above issue, most of them are noninteractive and static settings. Therefore, in practical applications, dynamic risk cannot be accurately assessed by data publishers, and even the noise protection causes an imbalance between privacy and utility. In this paper, we propose an interactive dynamic privacy risk assessment and adaptive protection method. It firstly utilizes machine learning prediction models to determine the privacy level of difficulttodefine attributes. And secondly we sets a mechanism to dynamically adjust the privacy level according to the actual requests of the data demander and timely restricts the leakage of sensitive attribute caused by highfrequency access. Finally, an adaptive noiseadding mechanism is proposed after obtaining the high risk of evaluation feedback, to ensure that the balance of privacy and utility achieveing good results when the data is released. Experimental results show that the privacy risk index increases gradually with the number of accesses and the accesses containing associated attributes lead to an increase in the privacy risk index by more than 15%. Adaptive noise addition, on the other hand, provides a similar level of privacy protection to overall noise addition at 0.5, yet improves data utility by more than 30% over overall noise addition. Reference | Related Articles | Metrics

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Research and Progress of the Cyber Security Standardization of Smart City Journal of Information Security Research    2016, 2 (5): 442-446.   Abstract （480）      PDF （4021KB）（622）       Knowledge map    Save The security risks of smart city are the important problems with the development of smart city. The paper introduces cyber security policies of China, the report of ISOIEC JTC1 SG1, the security architecture of smart city proposed by ITUT FG SSC, the overview of Smart America, and the work on cyber security standardization of smart city of TC260. The paper suggests how we develop the cyber security standards of smart city in China. Reference | Related Articles | Metrics

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Overview on Public Key Crytographic Algorithm SM2 Based on Elliptic Curves Journal of Information Security Research    2016, 2 (11): 972-982.   Abstract （1817）      PDF （7813KB）（993）       Knowledge map    Save Public key cryptographic algorithm SM2 based on elliptic curves (SM2 algorithm for abbreviation) was firstly issued in December 2010, had become the Chinese commercial cryptographic standard (GMT 0003—2012) in 2012, and had become the Chinese national cryptographic standard (GBT 32918—2016) in 2016. This paper briefly describe the development background of SM2 algorithm，describe SM2 algorithm in details，introduce the researches on its security, and evaluate its implementation efficiencies. All the researches on SM2 algorithm so far indicate that the provable securities of SM2 algorithm reach the supreme levels of public key cryptographic algorithms securities, and its implementation efficiencies are equivalent to or slightly superior to those similar elliptic curve cryptographic algorithms in some international standards. Reference | Related Articles | Metrics

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PeopleNet, Independently Developing Core Technology of Cyber Information Security Journal of Information Security Research    2017, 3 (7): 578-588.   Abstract （454）      PDF （1448KB）（866）       Knowledge map    Save Related Articles | Metrics

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DBAPPSecurity：Support Security China, Boost Digital Economy Journal of Information Security Research    2019, 5 (4): 274-281.   Abstract （212）      PDF （3884KB）（817）       Knowledge map    Save Related Articles | Metrics

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Research on Maintenance and Security of Industrial Control Networks in Electric Power Industry Journal of Information Security Research    2019, 5 (8): 679-684.   Abstract （267）      PDF （2038KB）（695）       Knowledge map    Save As an important part of national key infrastructure, the importance of operation and maintenance security of electric power industry control network is selfevident. Especially with the increasing security incidents of industrial control networks in the world in recent years, effective measures must be taken to protect the safe operation of industrial control networks, which also puts forward higher requirements for the operation, maintenance and safety protection of industrial control networks and industrial systems. Through indepth analysis of the characteristics of industrial control network in electric power industry, especially the key characteristics of the data type and network topology structure of the electric power network, effective operation and maintenance methods and security risk prevention methods are put forward. In operation and maintenance, the backup of system data and the state monitoring of the system itself are strengthened. Security measures, such as physical isolation, industrial control flow monitoring, fault recovery management and so on should be taken, and effective policies and behavioral norms should be provided. Finally, form safety protection measures suitable for electric power industry control network, to achieve the purpose of safe operation of the electric power industry control network. Reference | Related Articles | Metrics

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An Overview of Application and Technology of Artificial Intelligence in Cybersecurity Journal of Information Security Reserach    2022, 8 (2): 110-.   Abstract （2185）      PDF （1142KB）（1498）       Knowledge map    Save Compared with the developed countries, the basic research and technology application in the field of artificial intelligence in China started later, especially the application of artificial intelligence in the important field of network security. Domestic and abroad disparity is still very obvious, which seriously affects the improvement of China's cybersecurity capability. This paper elaborates the relationship between artificial intelligence, network attack and network defense, and widely investigates the application status of artificial intelligence in major information security companies at home and abroad. It points out that APT detection, 0day vulnerability mining and cloud security are three core areas that affect the level of cybersecurity capability, This paper deeply analyzes the key technologies of artificial intelligence technology applied in these three fields, and puts forward the safety risks of artificial intelligence technology, and points out that artificial intelligence technology is not a panacea for all diseases, This Paper provides a scientific reference for the further research and application of artificial intelligence technology in China's information security industry. Reference | Related Articles | Metrics

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A Survey on the Application of LSTM in Malicious Code Detection Journal of Information Security Reserach    2026, 12 (1): 89-.   Abstract （94）      PDF （9291KB）（78）       Knowledge map    Save With the continuous evolution of hacking technology, the iterative upgrades of malicious code variants have been acclerating and the number of malicious codes has exploded. How to rapidly and accurately detect malicious code has become a challenging research hotspot in the realm of cybersecurity. The unique gating mechanism of long shortterm memory network (LSTM) can selectively retain important historical information. Moreover, it demonstrates excellent performance for the sequential dependence of data on time series, which can effectively solve the problem of gradient vanishing or gradient explosion that may occur when traditional RNNs deal with such problems. This distinctive sequential processing capability of LSTM is particularly important for malware detection, thus learning to its extensive application in this area. This paper comprehensively sorts out and summarizes the application of LSTM in malicious code detection from five aspects, including the detection method of malicious code, the basic model and variants of LSTM, the application of LSTM in malicious code detection, the performance analysis of LSTM in malicious code detection, and the future development direction of LSTM in the field of malicious code detection, aiming to facilitating further research and improvement of existing methods for malicious code detection. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2026, 12 (2): 98-.   Abstract （197）      PDF （532KB）（134）       Knowledge map    Save Related Articles | Metrics

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Multidomain Fake News Detection Model Based on Prompt Learning and Fuzzy Labels Journal of Information Security Reserach    2026, 12 (7): 625-633.   Abstract （75）      PDF （1230KB）（18）       Knowledge map    Save The widespread popularity of the Internet and intelligent devices has provided convenience for the public to access news. However, this also creates a breeding ground for the generation and propagation of fake news. Fake news spans multiple domains, whereas existing detection models often overlook the specificity of corpora across different domains, limiting their accuracy. To adress this issue, this paper proposes a multidomain fake news detection model based on prompt learning and fuzzy labels. This model employs RoBERTa to extract textual features and reformulates the detection task as a cloze problem by constructing prompt templates containing domain characteristics; meanwhile, it utilizes domain fuzzy membership probabilities generated by a neural network to guide the prompt learning process, effectively enhancing accuracy and generalization ability. Experimental results on the public datasets Weibo17 and Weibo21 demonstrate that this model outperforms traditional finetuning methods and existing stateoftheart methods under both domainunlabeled and multidomain conditions, with an average F1 score improvement of 1.16 percentage points, validating its feasibility and effectiveness in multidomain fake news detection tasks. 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 （219）      PDF （682KB）（9）       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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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 （149）      PDF （3915KB）（9）       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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Three-Dimensional Way of Acorn Network in Industrial Control Cybersecurity Journal of Information Security Research    2017, 3 (8): 0-0.   Abstract （511）      PDF （3703KB）（838）       Knowledge map    Save Related Articles | Metrics

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Application of Network Security Situational Awareness Platform Based on Big Data in the Field of Private Network Journal of Information Security Research    2019, 5 (2): 168-175.   Abstract （273）      PDF （1678KB）（563）       Knowledge map    Save In order to improve the information security defense capability of the private network, the institutions with private network pay more and more attention to the information network security situational perception technology to realize the prediction and prevention of security events. Based on the brief introduction of situational awareness and related technologies, this paper puts forward a set of applicable network security situational awareness functional architecture targeting industryspecific network needs. This paper details the functional elements contained in each system from the functional level, which would provide reference for relevant institutions to build a network security situational awareness platforms. Reference | Related Articles | Metrics

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Flow Anomaly Detection Based on Hierarchical Clustering Method Journal of Information Security Research    2020, 6 (6): 0-0.   Abstract （1306）      PDF （1784KB）（722）       Knowledge map    Save With the advent of the big data era, the attacks in network traffic are rising dramatically. Detecting malicious traffic through abnormal flow detection is vital. Nowadays, the equipment of abnormal flow detection used in industry mainly adopts statistical analysis method or simple machine learning method. However, the amount of flow data and redundant data is large. The precision rate is low and the false alarm rate is high. In order to solve these problems, this paper presents a new method to detect flow anomalies based on hierarchical clustering in data processing. This method first uses the hierarchical clustering algorithm to achieve the purpose of data reduction. Then based on seven different machine learning algorithms, an abnormal traffic model based on hierarchical clustering is constructed. The experimental results show that this method can detect the abnormal behavior on the DARPA dataset with a precision rate of 99% and a recall rate of 99%. At the same time, while maintaining the precision rate of 90%, the data reduction can be up to 47.58%, which greatly improves the detection efficiency. Related Articles | Metrics

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Research on Browser Security and Trusted Architecture Under Xinchuang System Journal of Information Security Reserach    2021, 7 (4): 328-334.   Abstract （324）      PDF （2293KB）（307）       Knowledge map    Save With the rapid development of global informatization, the whole world is rapidly merging into one. A large number of information systems have become the key infrastructure of the country and the government. Many enterprises, organizations, government departments and institutions are building and developing their own networks and connecting them to fully share and utilize the information and resources of the network. The whole country and society are more and more dependent on the network. The network has become a powerful driving force for social and economic development, and its status is becoming more and more important. However, when resource sharing is widely used in political, military, economic and scientific fields, there are also various problems, especially security issues. Therefore, it is of great strategic significance in the process of informatization. This paper focuses on the current development of browsers, the security threats faced by browsers, and the security capabilities that browsers should have under the Xinchuang system. As the interface between users and the network information world, this paper still discusses the security solution of browser under the information innovation system. Reference | Related Articles | Metrics

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Overview of Data Security Governance at Home and Abroad Journal of Information Security Reserach    2021, 7 (10): 922-.   Abstract （1873）      PDF （3579KB）（1079）       Knowledge map    Save With the rapid development of digital economy, privacy infringement, data leakage, platform monopoly, misinformation and other issues emerge one after another, increasingly becoming an important issue that threatens individual rights, industrial development and national security. This article, on the national policy and law level, sorts out four categories of data governance, that is, personal data protection, cross-border data flow regulation, data market governance, and data content management. Countries and regions like United States, European Union and China are the centers of global digital economy. This article summarizes their practices and experience in above-mentioned four categories, and on this basis, puts forward some suggestions on strengthening China's data security governance system and capacity building, that is, further improving the legal system to compete for the leadership of the digital economy, deeply participating in global data governance to enhance the international voice of rule-making, and strengthening support and oversight of new technologies and applications to seize new heights in digital economy governance. Reference | Related Articles | Metrics

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Research on Source Code Vulnerability Detection Based on BERT Model Journal of Information Security Reserach    2024, 10 (4): 294-.   Abstract （498）      PDF （3199KB）（310）       Knowledge map    Save Techniques such as code metrics, machine learning, and deep learning are commonly employed in source code vulnerability detection. However, these techniques have problems, such as their inability to retain the syntactic and semantic information of the source code and the requirement of extensive expert knowledge to define vulnerability features. To cope with the problems of existing techniques, this paper proposed a source code vulnerability detection model based on BERT(bidirectional encoder representations from transformers) model. The model splits the source code to be detected into multiple small samples, converted each small sample into the form of approximate natural language, realized the automatic extraction of vulnerability features in the source code through the BERT model, and then trained a vulnerability classifier with good performance to realize the detection of multiple types of vulnerabilities in Python language. The model achieved an average detection accuracy of 99.2%, precision of 97.2%, recall of 96.2%, and an F1 score of 96.7% across various vulnerability types. This represents a performance improvement of 2% to 14% over existing vulnerability detection methods. The experimental results showed that the model was a general, lightweight and scalable vulnerability detection method. Reference | Related Articles | Metrics

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Federated Foundation Model Finetuning Based on Differential Privacy#br# #br# Journal of Information Security Reserach    2024, 10 (7): 616-.   Abstract （594）      PDF （1752KB）（286）       Knowledge map    Save As the availability of private data decreases, large model finetuning based on federated learning has become a research area of great concern. Although federated learning itself has a certain degree of privacy protection, privacy security issues such as gradient leakage attacks and embedding inversion attacks on large models still threaten the sensitive information of participants. In the current context of increasing awareness of privacy protection, these potential privacy risks have significantly hindered the promotion of large model finetuning based on federated learning in practical applications. Therefore, this paper proposes a federated large model embedding differential privacy control algorithm, which adds controllable random noise to the embedded model of the large model during efficient parameter finetuning process through a global and local dual privacy control mechanism to enhance the privacy protection ability of federated learning based large model parameter finetuning. In addition, this paper demonstrates the privacy protection effect of this algorithm in large model finetuning through experimental comparisons of different federation settings, and verifies the feasibility of the algorithm through performance comparison experiments between centralization and federation. Reference | Related Articles | Metrics

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Blockchain Security Sharding Scheme Based on Multi-dimensional Reputation Journal of Information Security Reserach    2024, 10 (8): 690-.   Abstract （374）      PDF （2816KB）（238）       Knowledge map    Save Blockchain faces scalability issues. Sharding improves system performance by dividing the blockchain network into multiple subnetworks that process transactions in parallel. However, sharding can lead to the clustering of malicious nodes, resulting in 51% attacks and affecting system security. The existing singledimensional reputation schemes have the problems of high overhead and insufficient shard consensus in the redistribution process, failing to ensure both performance and security. To address these  problems, a blockchain security sharding scheme based on multidimensional reputation is proposed: Firstly, the scheme integrates multidimensional indicators of nodes to balance shard reputation and computational communication abilities, identifying malicious nodes.  Secondly, a twostage redistribution scheme is proposed to reduce the frequency and cost of redistribution through partial redistribution in first stage and complete redistribution in second stage. Finally, a multidimensional reputation based fast Byzantine faulttolerant consensus (MRFBFT) is designed, which combines voting power and reputation, and introduces consensus among shard leader nodes to prevent malicious behavior. The experimental results show that the shard reputation and computational communication level are more balanced, the consensus delay is reduced by about 20%, and the throughput is increased by about 15%. Reference | Related Articles | Metrics

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A Review of Adversarial Attack on Autonomous Driving Perception System Journal of Information Security Reserach    2024, 10 (9): 786-.   Abstract （623）      PDF （1560KB）（323）       Knowledge map    Save The autonomous driving perception system collects surrounding environmental information through various sensors and processes this data to detect vehicles, pedestrians and obstacles, providing realtime foundational data for subsequent control and decisionmaking functions. Since sensors are directly connected to the external environment and often lack the ability to discern the credibility of inputs, the perception systems are  potential targets for various attacks. Among these, adversarial example attack is a mainstream attack method characterized by high concealment and harm. Attackers manipulate or forge input data of the perception system to deceive the perception algorithms, leading to incorrect output results by the system. Based on the research of existing relevant literature, this paper systematically summarizes the working methods of the autonomous driving perception system, analyzes the adversarial example attack schemes and defense strategies targeting the perception system. In particular, this paper subdivide the adversarial examples for the autonomous driving perception system into signalbased adversarial example attack scheme and objectbased adversarial example attack scheme. Additionally, the paper comprehensively discusses defense strategy of the adversarial example attack for the perception system, and subdivide it into anomaly detection, model defense, and physical defense. Finally, this paper prospects the future research directions of adversarial example attack targeting autonomous driving perception systems. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2025, 11 (E2): 89-.   Abstract （135）      PDF （1508KB）（71）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Smart Contract Vulnerability Detection Method Based on  Multimodal Feature Fusion Journal of Information Security Reserach    2026, 12 (6): 503-.   Abstract （102）      PDF （1602KB）（63）       Knowledge map    Save Most of the smart contract vulnerability detection methods rely on single mode feature extraction, which leads to the problem of low detection accuracy due to insufficient key feature extraction. This paper proposes a smart contract vulnerability detection method based on multimodal feature fusion. Firstly, the construction of the control flow graph (CFG) is constructed by leveraging the abstract syntax tree (AST) trimmed at the source code layer and the data flow relationship based on the opcode layer, which is imported into the graph attention network (GAT) to extract two types of static features. Secondly, the fuzzing test report generated by echidna, a dynamic detection tool, is used to extract path coverage, state changes and other information to build a graph model, and the dynamic features are extracted by graph neural network (GNN). Finally, the extracted static and dynamic features are fused and input into CNN bilstm att model for vulnerability detection, and relevant experiments are carried out on 47398 smart contracts. Experimental results show that compared with eight mainstream detection methods, such as SmartCheck, Mythril, Oyente, BiGGNN, ASTNN, DRGCN, SVCB and CBGRU, the accuracy, recall and F1 value of this method in reentry vulnerability, timestamp vulnerability, integer overflow vulnerability and Tx.origin vulnerability are increased by 50.26%, 59.54% and 58.40%. Reference | Related Articles | Metrics

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“Internet +”Power: Overview of Westone Secruity’s Cyber Secruity Journal of Information Security Research    2016, 2 (10): 862-875.   Abstract （400）      PDF （2788KB）（1147）       Knowledge map    Save Related Articles | Metrics

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Research of Emergency Resources Big Data Cloud Security Management Han Xiaolu1 and Lü Xin2 Journal of Information Security Research