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LLMenhanced Static Analysis for Detecting Broken Object Level Authorization Vulnerabilities in Java Web Applications#br# #br# Journal of Information Security Reserach    2026, 12 (5): 394-.   Abstract （55）      PDF （1497KB）（48）       Knowledge map    Save Broken object level authorization (BOLA) is currently one of the critical security threats to Web applications. As a typical unauthorized access vulnerability, BOLA arises when a system fails to properly validate a user’s access permissions to target objects. The key to static detection of BOLA vulnerabilities lies in: accurately identifying objectlevel sensitive operations and analyzing unprotected access behaviors during path traversal. Since BOLA is an application logiclevel vulnerability, its detection effectiveness directly depends on the precision of understanding the expected objectlevel authorization policies. However, existing detection methods predominantly rely on empirical heuristic rules to identify sensitive and protected operations, making them difficult to adapt to the actual business logic of different applications, resulting in high false positives and false negatives in detection results. To address this limitation, this paper innovatively proposes a large language model (LLM)enhanced static detection method for BOLA vulnerabilities in Web applications, LLM4BOLA. First, leveraging LLM’s advanced code comprehension and semantic reasoning capabilities to infer objectlevel sensitive operations and custom authorization policies in specific business scenarios. Then, identifying diverse permission protection mechanisms. Finally, comprehensively detecting missing objectlevel permission checks along the paths from request entry points to sensitive operations. Experimental results demonstrate that the proposed method not only effectively detects known vulnerabilities but also discovers unknown ones, significantly outperforming traditional rulebased approaches in detection accuracy. Reference | Related Articles | Metrics

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OSN Intrusion Detection Method Based on Residual Timeattention with Feature Selection#br# #br# Journal of Information Security Reserach    2026, 12 (5): 402-.   Abstract （32）      PDF （1781KB）（31）       Knowledge map    Save Online social network (OSN), as core platform for information exchange, currently face serious intrusion threats. However, existing OSN intrusion detection techniques exhibit poor detection performance when dealing with issues such as high dimensionality, diverse datasets with different types of structures, significant semantic differences, and mismatched dynamic features. Therefore, an intrusion detection method based on residual timeattention with feature selection (RTAS) is proposed. The method utilizes the pretrained language model BERT for data preprocessing and designs a classifier based on residual timeattention. The model effectively captures contextual features in a wide range of text information through a bidirectional LSTM and attention mechanism. Meanwhile, an adaptive feature selection method based on deep reinforcement learning is proposed, which utilizes adaptive learning to obtain the optimal feature set. The experiment shows that the proposed method achieves accuracies of 98.53%, 98.68%, and 98.33% in detecting multiple threat patterns on datasets from Facebook, Google+, and Twitter, respectively. The average accuracy on the three datasets exceeds other mainstream methods. Reference | Related Articles | Metrics

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Research on Dataenhanced Multimodal False Information #br# Detection Framework#br# Journal of Information Security Reserach    2025, 11 (4): 377-.   Abstract （156）      PDF （1878KB）（67）       Knowledge map    Save With the development of multimedia technology, rumor spreaders tend to create false information with multimodal content to attract the attention of news readers. However, it is challenging to extract features from sparsely annotated multimodal data and effectively integrate implicit clues in the multimodal data to generate vector representations of false information. To address this issue, we propose a DEMF(dataenhanced multimodal false information detection framework). DEMF leverages the advantages of pretrained models and data augmentation techniques to reduce reliance on annotated data; it utilizes multilevel modal feature extraction and fusion to simultaneously capture finegrained elementlevel relationships and coarsegrained modallevel relationships, in order to fully extracting multimodal clues. Experiments on realworld datasets show that DEMF significantly outperforms stateoftheart baseline models. Reference | Related Articles | Metrics

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Research on the Implementation Path of Zero Trust Strategy Journal of Information Security Reserach    2026, 12 (5): 483-.   Abstract （27）      PDF （3588KB）（28）       Knowledge map    Save Amid the wave of digital transformation, the traditional boundarybased network security model is increasingly ineffective in dynamic and border less environments. The United States has taken the lead in restructuring its cybersquatting system through a systematic zerotrust strategy, and its trinity practice path of “policytechnologyecology” is of reference significance for China to build a digital security barrier. This paper uses case analysis and policy comparison methods to deeply analyze the toplevel design logic, core technological breakthrough points, and ecological coordination mechanisms of the U.S. zerotrust strategy, revealing its essence of transitioning from “passive protection” to “active immunity”. Based on a deep diagnosis of the complexity of China’s ultralargescale network ecosystem, the shortcomings in the autonomy of core technologies, and the challenges of data sovereignty governance, this paper proposes a Chinesestyle “fourdimensional integrated” implementation path: breaking the fragmented dilemma with systematic toplevel design; breaking through technological bottlenecks with the integration of national cryptography and AIdriven technologies; building a security ecosystem with costsharing and standard leadership through governmententerprise collaboration; and addressing implementation limitations with scenario classification and privacy enhancement. The study emphasizes that China needs to innovate on the basis of reference, take zero trust as an important engine for building a digital security barrier, and balance the needs of security protection with the development of the digital economy. Reference | Related Articles | Metrics

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Research on the Risk Assessment System for Data Security in the Transportation Industry#br# Journal of Information Security Reserach    2025, 11 (11): 1064-.   Abstract （86）      PDF （5683KB）（62）       Knowledge map    Save The transportation information system is not only a critical component of the national key information infrastructure, but also an important industry of the 2+8+N system, in which is crucial to the nation’s economy and people’s livelihood. With the continuous advancement of smart transportation construction, the volume of traffic data is growing rapidly. The position of data as a production factor highlights its importance and value, and also induces higher demands for data security. The transportation industry pays close attention to data security, and based on national policies, laws, regulations, and standards, the competent transportation authorities have issued a series of industry standards and regulations to guide the security of transportation data. However, the transportation industry covers a wide range of business areas, and its data has characteristics such as multisource, heterogeneity, partiality, spatiotemporal correlation, asynchronicity, information sparsity, and concurrency. Moreover, the data has a high degree of mobility, and the operational conditions and flow are complex, making data surveillance a large range and great difficulty, which brings a series of challenges to the protection of data security. Based on existing laws, regulations, and standards, and deeply integrating the characteristics of transportation industry data, this research on the data security risk assessment system provides a reference for the construction of transportation data security protection. Reference | Related Articles | Metrics

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Research on Harmful Website Detection Based on Graph Neural Network and Multifeature Fusion Journal of Information Security Reserach    2026, 12 (5): 420-.   Abstract （29）      PDF （1884KB）（27）       Knowledge map    Save To address the limitations of current harmful website detection methods in deep text semantic mining and multimodal feature coperception, this study proposes a multifeature fusion detection model based on graph attention networks (GAT) and ConvNeXt. The framework leverages GloVe word embeddings to construct semantic representations of website text, mapping it into a graph structure based on word cooccurrence relationships. The adaptive attention mechanism in GAT dynamically captures contextual dependencies between noncontiguous words, while ConvNeXt extracts both local details and global contextual features from website images. A crossattentionbased fusion module facilitates dynamic textimage feature alignment and interactive integration. Experimental results demonstrate that the proposed model achieves 99.10% accuracy in fourcategory website classification, significantly enhancing detection performance. This work offers valuable insights for identifying harmful online content and enhancing cybersecurity governance. Reference | Related Articles | Metrics

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Smartphone Image Recovery and Forensics Based on WinHex Journal of Information Security Research    2016, 2 (4): 328-332.   Abstract （497）      PDF （4459KB）（751）       Knowledge map    Save Smartphone has gradually become one of important sources of information in the current electronic forensics investigation. Aiming at the difficult problem of information acquisition when the picture of the Android smartphone was deleted or damaged, a file recovery method based on WinHex tools is provided. In experiments, by creating a cell phone store image and the header and tail sign of file, the phone's image files were extracted. Reference | Related Articles | Metrics

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Building Cyber Security Defense by Trusted Computing 3.0 Journal of Information Security Research    2017, 3 (4): 290-298.   Abstract （430）      PDF （1075KB）（2017）       Knowledge map    Save Related Articles | Metrics

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Research on the Institutional Framework and Rule Characteristics of Chinese Network Information Security Policies Journal of Information Security Reserach    2026, 12 (5): 474-.   Abstract （25）      PDF （1709KB）（25）       Knowledge map    Save Network information security policy is an important institutional guarantee for maintaining national security and development, and analyzing the institutional framework and rule characteristics of Chinese network information security policies is of great significance for enhancing its implementation effect of network information security policies. Coding statistics of 39 network information security policies, based on the components of institutional grammar tool (IGT), this paper highlights the complex orientation of the institutional framework of Chinese network information security policies from five dimensions: actors, action constraints, action scenarios, action goals, and evaluation results. Based on the framework of the system，focusing on the function definition rules, exit access rules, decisionmaking guidance rules, information transfer rules, and rewards and punishments incentives rules in the institutional analysis and development (IAD) framework, it is found that the current Chinese network information security policies are characterized by the rules of ambiguous definition of departmental rights and responsibilities, missing access conditions of the main body, weakening of the statement of the regulatory strength, poor reverse feedback channels, and unequal distribution of the rewards and punishments. In view of existing problems, in the future, the effectiveness of regulation can be improved by clarifying the rights and responsibilities of departments, adding access conditions, adding rulebased statements, optimizing feedback channels, and improving incentive and punishment measures. Reference | Related Articles | Metrics

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Design and Implementation of Resourceefficient SM4 Algorithm on FPGA Journal of Information Security Reserach    2025, 11 (6): 490-.   Abstract （266）      PDF （2238KB）（128）       Knowledge map    Save In the hardware implementation of the SM4 algorithm, the lookup table method is commonly adopted for realizing the Sbox, which consumes a significant amount of hardware resources. This paper proposes an implementation scheme for the SM4 algorithm based on polynomial basis. Two construction schemes are developed for the 8×8 Sbox used in the SM4 algorithm, one based on composite field GF((24)2) and the other on composite field GF(((22)2)2). The test results indicate that the scheme based on polynomial bases GF((24)2) is optimal. Taking into account both resource utilization and performance, this paper designs two hardware implementation structures for SM4: a state machine parallel structure and a pipelined structure. Compared with the traditional lookup table approach, the state machine parallel structure reduces resource utilization by 21.98% while increasing the operating frequency by 14.4%. The pipelined structure achieves a reduction in resource utilization by 54.23%. Reference | Related Articles | Metrics

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A Deceptionresistant Multilevel Visual Cryptography Scheme  Based on Random Grids Journal of Information Security Reserach    2025, 11 (6): 532-.   Abstract （182）      PDF （2395KB）（58）       Knowledge map    Save Visual cryptography is a technique for encryption by dividing a secret image into n shares and recovering the image by superimposing the shares. However, there may be deceptive behavior during the process of reconstructing the secret image. This paper proposed a multi-level visual cryptography scheme based on random grids to address this problem. The scheme introduces a trusted third party (TTP) to address this issue. The scheme verified XOR on shares at each level during secret distribution and recovery processes. Experimental results demonstrate that the proposed scheme effectively detects deception by distributors and participants, enhancing security during implementation. Reference | Related Articles | Metrics

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Design and Implementation of 3D Model Matching Algorithm Journal of Information Security Reserach    2025, 11 (6): 539-.   Abstract （213）      PDF （2587KB）（46）       Knowledge map    Save 3D model matching plays a vital role in model copyright protection and transaction facilitation by effectively preventing redundant authentication and enabling convenience for research, testing, and management in related fields. However, traditional matching approaches predominantly rely on plaintext matching, which, despite ensuring a certain level of matching accuracy and robustness, falls short in data privacy protection. To address this gap, ciphertext matching performs matching computations on encrypted data, thus enabling model matching while safeguarding data privacy. This approach offers significant practical value and broad application prospects. Therefore, this paper presents three matching strategies. 1) Under plaintext conditions, precise registration of 3D point clouds is achieved via the Iterative Closest Point (ICP) algorithm, followed by model matching using peak signaltonoise ratio (PSNR). 2) Under plaintext conditions, 3D point cloud features are extracted using the PointNet deep learning model, and feature similarity is calculated via cosine similarity. 3) Under ciphertext conditions, the extracted features are encrypted using homomorphic encryption. Cosine similarity is then used to compute the similarity of the encrypted features, thereby effectively protecting data privacy. Reference | Related Articles | Metrics

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Dualbranch Malicious Code Homology Analysis Model Based on Feature Fusion Journal of Information Security Reserach    2025, 11 (7): 594-.   Abstract （147）      PDF （2563KB）（66）       Knowledge map    Save In the homology analysis of malicious code, a large number of malicious code variants are generated due to techniques such as encryption, obfuscation, and packing, which leads to the problem that the deep learning model has insufficient ability to extract the features of malicious code. To solve this problem, a multibranch convolution and transformernet (MCATNet) homology analysis model based on feature fusion was proposed. Firstly, an MCATNet dualbranch network was constructed, one branch was a multibranch convolutional MBC (Multibranch convolution) module, and the MBC module was used to construct the CNN branch, and the CBAM hybrid attention mechanism was introduced to make the network pay more attention to the core features while taking into account the local features. Another branch is the Transformer module with ViT as the backbone, which extracts global feature information of malicious code images and proposes a downsampling module to finely preserve global features while aligning the feature maps of Transformer and CNN at the spatial scale. Secondly, the cascading strategy is used to fuse the local features of the CNN branch and the global features of the Transformer branch to solve the problem that the network only focuses on a single feature. Finally, the Softmax classifier was used to analyze the homology of the malicious code family. Experimental results show that the classification accuracy of the twobranch model based on feature fusion reaches 99.24%, which is 0.11% and 0.65% higher than that of the singlebranch CNN and singlebranch Transformer models, respectively. Reference | Related Articles | Metrics

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A Rapid Method for WebShell Attack Success Determination Based on Web Page Structural Similarity Journal of Information Security Reserach    2026, 12 (3): 255-.   Abstract （37）      PDF （1396KB）（49）       Knowledge map    Save WebShell attack, a type of network attack, can control the website completely for a long time after a successful attack, which is extremely harmful. Most of the previous studies have concentrated on detecting and alerting WebShell attack traffic without distinguishing whether the attack is ultimately successful. As a result, in actual network security protection and monitoring work, security personnel are overwhelmed by a large number of WebShell attack alerts and are prone to alert fatigue, making it difficult to filter out successful WebShell attacks which are truly threatening. To address the problem, this paper proposes an anomaly detection method based on Web page structural similarity to quickly determine whether WebShell attacks are successful. Based on the structural information of the response pages of failed WebShell attack traffic, this method uses the HuntSzymanski algorithm to calculate structural similarity and then generate Web page templates. During the detection phase, this method uses the generated Web page templates for pattern matching and similarity assessment to determine whether the WebShell attacks are successful. It can well distinguish between successful and failed WebShell attack traffic, achieving an accuracy rate of 99.02% and a recall rate of 99.37%. This method has been applied to Wukong network security defense system and realizes rapid identification of successful WebShell attacks. Reference | Related Articles | Metrics

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Generative Logic and Coping Strategies of Personal Information Security Risks in Digital Platform Journal of Information Security Reserach    2026, 12 (5): 445-.   Abstract （37）      PDF （1235KB）（23）       Knowledge map    Save While digital platform provides tremendous convenience for public production and daily life, security risks such as personal information leakage and misuse have simultaneously escalated. As a new tier in governance structures, guiding digital platform to strike a balance between information protection and data openness is crucial for advancing the modernization of cyberspace governance systems and enhancing governance capacity. Examining digital platform from the perspective of data controllers, this paper explores the generative logic of personal information security risks through a threetiered framework: unauthorized collection, unregulated processing and improper application. The paper proposes coping strategies including refining “informed consent” operational details to stabilize the privacy policy framework of platform, strengthening the application of data desensitization technologies to standardize the automated decisionmaking processes of platform, and improving the information provision and disclosure mechanisms to enhance the internal information management of platform, so as to achieve a balance between personal information protection and the release of the value of data elements. Reference | Related Articles | Metrics

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Multiparty Data Security Sharing Scheme Based on Decentralized Verification Journal of Information Security Reserach    2025, 11 (6): 578-.   Abstract （203）      PDF （4817KB）（100）       Knowledge map    Save With the development of the Internet of Things, collaborative work between multiple devices is becoming increasingly common. However, in the process of data sharing, user privacy may face the risk of data theft and tampering. Existing FL methods rely on mobile edge computing (MEC) servers for model aggregation, and have problems with trust, security threats, and single points of failure. To solve these problems, a new multiparty data sharing scheme based on blockchain is proposed, in which a decentralized verification mechanism and a consensus mechanism inspired by proof of stake (PoS) are introduced. The decentralized verification mechanism ensures the legitimacy of each local model update by evaluating node behavior and voting, and only legitimate updates are used for global model building. In the process of model construction, homomorphic encryption and key sharing techniques are used to encrypt the local model parameters to ensure the security integrity of model parameters in the process of transmission and aggregation. The PoS consensus mechanism rewards honest behavior devices, increasing their chances of becoming block generators. In addition, the cache mechanism of information search is introduced to reduce the number of multiparty search. The data sharing scheme has been verified to enhance data security. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2026, 12 (2): 98-.   Abstract （127）      PDF （532KB）（115）       Knowledge map    Save Related Articles | Metrics

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Research on Large Model Security Assessment Technology Based on Group Polarization Nested Jailbreak Templates#br# #br# Journal of Information Security Reserach    2026, 12 (5): 410-.   Abstract （27）      PDF （2619KB）（21）       Knowledge map    Save As large model demonstrates excellent performance in natural language processing tasks, its security issues become increasingly prominent. Jailbreak attacks bypass model security mechanisms, weaken value alignment constraints, and induce models to generate harmful content. The risks of model abuse, hijacking, and information leakage caused by such attacks pose security threats to the large language model application ecosystem. To comprehensively evaluate large model security performance, a nested jailbreak template technique based on the group polarization psychological effect is proposed, which guides models to generate complex responses through progressively nested instructions. Based on this, the NesTHGA (nested templatehierarchical genetic algorithm) framework is constructed by integrating hierarchical genetic algorithms. Experimental results show that this method achieves an average attack success rate of over 80% across 8 mainstream large models, statistical tests confirm significant differences from existing methods, and ablation experiments verify component synergistic effects, effectively evaluating the security and robustness of large models against complex attacks. Reference | Related Articles | Metrics

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Promoting “Internet+”Action: Explanation to Cyber Security of JD Journal of Information Security Research    2016, 2 (1): 2-11.   Abstract （442）      PDF （1695KB）（1057）       Knowledge map    Save Related Articles | Metrics

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A Fully Homomorphic Encryption Scheme Based LWE Journal of Information Security Research    2016, 2 (3): 220-224.   Abstract （499）      PDF （3693KB）（715）       Knowledge map    Save Two main problems serve as foundations of current homomorphic encryption schemes: Regevs learning with errors problem (LWE) and HowgraveGrahams approximate greatest common divisor problem (AGCD). Firstly, we introduce a reduction from LWE to AGCD. Secondly, we propose a new AGCDbased additive homomorphic encryption scheme, which outperforms previous proposals: its security does not rely on the presumed hardness of the socalled sparse subset sum problem (SSSP), but on the hardness of LWE. Lastly, correctness analysis and security proof are given. Reference | Related Articles | Metrics

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The Improvement and Instance Analysis of the Formal Verification Tool Scyther Xu Han Journal of Information Security Research    2016, 2 (3): 272-279.   Abstract （679）      PDF （6016KB）（781）       Knowledge map    Save As formal verification tools for security protocols develop rapidly, picking a suitable tool, according to the target protocols and the security models, can improve not only the reliability and accuracy of protocol analysis, but also the efficiency of it. With these considerations, we compare the properties of 9 kinds of the formal verification tools in detail and we find that the Scyther tool can be an optimal choice in terms of interface interactivity, analysis efficiency and security model validity. In an attempt to facilitate using and researching of the tool for analysts in China, we study and analyze the underlying algorithm of Scyther and translate the interface into Chinese; we also update Scyther by adding a timer that can count and output the analysis time. Finally, we use the updated Scyther to make a formal analysis of the network security protocol TLS within the DelovYao model and strong security model. The current study is of theoretical and practical value for it helping researchers to select and use formal analysis tools more accurately and effectively. Related Articles | Metrics

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Malicious Behavior Detection Method Based on Behavior Clustering LSTMNN#br# Journal of Information Security Reserach    2025, 11 (4): 343-.   Abstract （166）      PDF （2288KB）（64）       Knowledge map    Save With the progress and development of society, the safety requirements for public places have further increased. Malicious behavior detection can monitor and identify potential safety hazards in real time. To solve this problem, the Kmeans clustering method is used to divide the molecular data set and distinguish different forms of malicious behavior. To solve this problem, the Kmeans clustering method is used to divide the subdatasets to distinguish different forms of malicious behaviors. The DTW time warping method solves the problem of inconsistent lengths of malicious behavior time series. In order to solve the problem of image recognition, the excessive amount of data in the malicious behavior frame set makes the model calculation accuracy low, and the Attention mechanism is used to focus on special information points to ensure the accuracy of model training. This method was applied to the malicious behavior data set of UBIFights. The results showed that the final classification accuracy of the subdataset after clustering division by weighted average calculation reached 95.03%. This model effectively identifies malicious behavior videos and improves safety. Reference | Related Articles | Metrics

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Task Independent Privacy Protection in Personalized Federated Learning  for Battery Monitoring Journal of Information Security Reserach    2025, 11 (5): 481-.   Abstract （172）      PDF （4140KB）（54）       Knowledge map    Save For the health management of batteries in new energy vehicles, it is essential to collaboratively share distributed battery data and establish a federated learning model to extract valuable information. To counteract the privacy leakage risks associated with battery data sharing, this paper designs a taskindependent privacy protection and communicationefficient federated learningempowered edge intelligence model. This model learns personalized subnetworks that generalize well to local data and uses network pruning to find the optimal subnetwork, ensuring inference accuracy. Meanwhile, to resist feature reconstruction attacks and privacy leakage risks, it constructs taskindependent privacyprotective anonymous intermediate representations. By employing adversarial training, it maximizes the reconstruction error of the adversarial reconstructor and the classification error of the adversarial classifier, while minimizing the classification error of the target classifier. Experimental simulations show that this method improves inference accuracy by 8.85%  and reduces communication overhead by 1.95 times. The balance analysis of utility and privacy demonstrates that it ensures the accuracy of target feature extraction while protecting privacy. Reference | Related Articles | Metrics

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PDF File Malicious Code Detection Method Based on Spectrum Analysis Hao Chenxi and Fang Yong Journal of Information Security Research

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Research on The Technology Architecture of Big Data Security and Privacy System Lv Xin Journal of Information Security Research    2016, 2 (3): 244-250.   Abstract （1022）      PDF （6172KB）（1055）       Knowledge map    Save In the era of Big Data, data security and privacy face a lot of technical challenges in the aspects of security architecture, data privacy, data management, integrity and proactive security protection. Based on the analysis of technical challenges of big data security and privacy protection, this paper proposes a reference model of the technology architecture of big data security and privacy system, and researches on the key technology and the latest progress of big data security and privacy, focusing on data layer security, application layer security, interface layer security and system layer security. It provides an effective technical reference for establishing big data security and privacy system. Reference | Related Articles | Metrics

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Time Machine Forensic Journal of Information Security Research    2016, 2 (4): 307-316.   Abstract （1125）      PDF （5953KB）（812）       Knowledge map    Save Time Machine is an automatic data backup tool in Mac OS. Mac users can backup their important programs and data continuously with different kinds of removable disk, such as USB removable hard disk, Firewire hard disk, Thunderbolt hard disk, Time Capsule. Especially in investigations and cases, it is important for investigators to find out if there are any backup storages in a WiFi network. A single Time Machine storage can save multiple users backup data, or even several Mac computers backup data. The normal deleted data in a Mac can even be foundrecovered from Time Machine backup. The important breakthrough may be from a Time Capsule, and the analytical result of Time Machine backup. This article will discuss Time Machine, backup storage, folder structure, and how to make forensics analysis manually and automatically. Reference | Related Articles | Metrics

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Management and Directed Study of Internet Public Opinion in the Age of Big Data Journal of Information Security Research    2016, 2 (4): 356-360.   Abstract （510）      PDF （4303KB）（731）       Knowledge map    Save As we step into the age of big data，Internet Public Opinion has undergone great changes in terms of data volume，complexity and production speed. In big data Era, its internal features and potential rules during the changing process should be controlled correctly. This paper has important theoretical significance and practical value in guiding internet public opinion and protecting cyber security under the new circumstances. In the Internet era of big data, with the help of computer technology, timely, comprehensive monitoring network public opinion is imminent. We would first discuss the new changes of network public opinion brought by big data, public opinion then elaborated coping strategies, At last, the paper introduces the design idea of the network public opinion monitoring system based on Crawler Web and Lucene. Reference | Related Articles | Metrics

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Analysis on the Trend of Cybersecurity Legislation in the Era of Big Data Journal of Information Security Research    2016, 2 (9): 815-820.   Abstract （324）      PDF （5093KB）（567）       Knowledge map    Save In the era of big data, the cyber security legislation is challenged by the development of technology and showing diversified trends—from “invasion” regulation to “attack” governance, from “three properties of cyber security” to “trusted key element”, from “IT security” to “CPS security”, from cyber security “content governance” to “stereoscopic” governance. In the content of legislation, we should pay attention to establish the allwave situational awareness of cyber security early warning, rational design of data localization legislative and cyber security review system and information sharing system for guard against network attack, establish the state of emergency strategic contingency plan, adjust the content security mode of thinking, guide the system design of cyber security legislation by “stereoscopic” mode of thinking. Reference | Related Articles | Metrics

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Defects and Breakthroughs of Current Cyberspace Security Technology System Journal of Information Security Research    2017, 3 (12): 1141-1144.   Abstract （231）      PDF （886KB）（453）       Knowledge map    Save Related Articles | Metrics

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A Trust Framework for Large Language Model Application Journal of Information Security Reserach    2024, 10 (12): 1153-.   Abstract （397）      PDF （1420KB）（267）       Knowledge map    Save The emergence of large language model has greatly propelled the rapid application of artificial intelligence across various domains. In practice, however, there are a series of security and trust challenges in the applications of large language models caused by “model hallucinations”. These challenges make it difficult for practical applications to trust and adopt the results returned by the large language models, especially in securityrelated application domains. In many professional fields, we find that there lacks a unified technical framework to ensure the trustworthiness of results returned by large language models, which seriously hinders the application of largescale model technology in professional fields. To address this issue, a largescale model trusted application framework DKCF, integrating sufficient data (D), expertise knowledge (K), intellectual collaboration (C), and efficient feedback (F), is proposed. This framework is developed based on our practical applications in professional fields such as finance, healthcare, and security. We believe that DKCF can shed light on secure and reliable applications of large language models, and facilitate the intellectual revolution across various professional domains. Reference | Related Articles | Metrics

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Lightweighted Mutual Authentication and Key Agreement in V2N IoV Journal of Information Security Reserach    2025, 11 (8): 753-.   Abstract （130）      PDF （2403KB）（56）       Knowledge map    Save Aiming at the scenario of vehicle secure access to application servers in the V2N (vehicle to network) environment, a Kerberos extension protocol is proposed based on the PUF (physical unclonable function). This protocol provides the twoway authentication and key agreement between the vehicle and the remoted application server and ensured the confidentiality and authentication of the V2N data transmission. The CRP (challenge response pair) generated by the PUF is used to replace the password in standard Kerberos to prevent the threats of key leakage caused by physical attacks such as intrusion, semiintrusion, sidechannel attacks, etc. The characteristics of Kerberos’s lightweighted twoway authentication protocol can overcome the defects of high calculation complexity and slow speed of the public key authentication algorithms, and effectively provide the secure data transmission between vehicles and application servers. Reference | Related Articles | Metrics

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Highorder Program Driven by Large Language Model Journal of Information Security Reserach    2025, 11 (11): 1008-.   Abstract （107）      PDF （2871KB）（36）       Knowledge map    Save Large language models (LLMs) often exhibit hallucinations in various occasions, leading to unreliable inferences. Such vulnerabilities render it  critical for LLMs to be adopted cautiously in vertical domains such as financial, medical, and cybersecurity domains. In preLLM era, humans have accumulated the best practices to ensure reliabilities of complicated tasks through careful engineering. Standard operating procedures (SOP) and Check List are the exemplars of these best practices. Likewise, in LLM era, we propose highorder program (HOP)to achieve the reliability breakthroughs. By fusing both accurate execution of traditional programing languages, and superior knowledge intrinsics of LLMs, HOP sets the backbone of the control system required by vertical LLM applications. HOP achieves automations by leveraging key vertical knowledge and practices. More importantly, it delivers expected reliability through verifications. HOP itself can be autogenerated by LLMs, which further incentivizes its wide adoptions. Lately, we have applied HOP in different scenarios including fulllifecycle financial risk management in cryptographic computing settings, duplicate charges in medical diagnosis, and intrusion detection. HOP has achieved 5 to 10 folds of efficiency improvement, and an accuracy as good as 99% across aforementioned scenarios. Reference | Related Articles | Metrics

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Research on Neural Networkbased Protocol Identification for Secure Multiparty Computation Journal of Information Security Reserach    2026, 12 (3): 228-.   Abstract （49）      PDF （1921KB）（51）       Knowledge map    Save Secure multiparty computation (SMPC) enables joint computation while keeping private data undisclosed, positioning it as a core technology in privacypreserving computing. However, its high computational complexity and substantial overhead render practical deployment reliant on cloud providers for computational resources. To meet the requirement of realtime protocol monitoring in privacypreserving computing scenarios on cloud platforms, this paper proposes a neural networkbased protocol identification scheme for SMPC. By collecting performance data from computation nodes, including CPU usage and network bandwidth usage, a 3D convolutional neural network (CNN) model integrating spatiotemporal feature extraction capabilities is constructed. This model, along with a dynamic threshold mechanism, enables highaccuracy classification of known protocols and anomaly detection of unknown protocols. Experimental results show that the model attains an accuracy of 98% on the validation dataset and a detection rate exceeding 98% for unknown protocols, thereby significantly improving the operational security and reliability of SMPC systems. Reference | Related Articles | Metrics

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Research on Cryptographic Access Control and Its Applications Journal of Information Security Research    2016, 2 (8): 721-728.   Abstract （575）      PDF （7726KB）（454）       Knowledge map    Save Cryptographic access control is a hot topic in recent years and has wide promising applications. This paper firstly analysed new threats and challenges for traditional access control methods in cloud computing. The cryptographic access control was categorized into symmetrickey cryptography based and public key cryptography based mechanisms, and main schemes of the two types were discussed later. Its application in encrypted file systems and securing cloud storage was introduced as well. Finally, this paper analysed main problems, challenges and further research directions for future discussion about cryptographic access control. Reference | Related Articles | Metrics

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Research on the Framework of Smart City Cyber Security Journal of Information Security Research    2016, 2 (9): 827-833.   Abstract （473）      PDF （7095KB）（334）       Knowledge map    Save In order to ensure the healthy and orderly development of smart city, the government issued the Guidance on Promoting the Healthy Development of Smart City. The guidance clearly puts forward the need to ensure longterm security of smart city network, establish urban cyber security guarantee system and management system, achieve security controllability of basic network and key information system, guarantee the security of important information resources, and effectively protect the information of residents, enterprises and government. This paper analyzes the characteristics and cyber security demands of smart city. Based on system engineering method, this paper adopts model abstract and AHP method to put forward the smart city cyber security reference framework. According to the functions of assurance, smart city cyber security assurance system can be divided into smart city cyber security strategic assurance subsystem, smart city cyber security organizational assurance subsystem, smart city cyber security technical assurance subsystem, smart city cyber security management procedural subsystem, and smart city cyber security operational assurance subsystem. By building a complete and systemic smart city cyber security assurance system, a manageable, controllable and credible smart city network can be realized. The assurance system will boost the healthy development of smart city. In addition, this paper also studies the process model of smart city cyber security assurance, which will provide technical reference for the establishment of sustainable smart city cyber security assurance capability. Reference | Related Articles | Metrics

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Research on the Ethical and Legal Risk of Artificial Intelligence Journal of Information Security Research    2017, 3 (12): 1080-1090.   Abstract （454）      PDF （9629KB）（283）       Knowledge map    Save The concept of "artificial intelligence" since 1956, puts forward , artificial intelligence technology fast development, the comprehensive promotion, artificial intelligence application in computer network, electronic science and technology, information engineering, and many other fields are widely used, in promoting the network of information technology at the same time, also has a far-reaching influence on all aspects of social life, for medical, communications, transportation, finance, etc many fields provides convenient technical support.In 2016 as the unprecedented development of artificial intelligent technology for a year, was known as the "inflection point" of artificial intelligence, but as a new technology of the Internet era, intelligence of artificial intelligence in the development of network information technology at the same time, it blurred the line between physical reality, numbers, and personal, also spawned many complex legal and ethical issues, we need to deal with in time, make artificial intelligence a greater advantage of its positive value.This paper starting from the concept review and development history of artificial intelligence, based on the present situation of the development of artificial intelligent technology, the problems of legal risk source analysis technology development, proposes the corresponding solution measures. Reference | Related Articles | Metrics

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Network Coding Hybrid Encryption Scheme for IoT System Journal of Information Security Reserach    2025, 11 (4): 326-.   Abstract （163）      PDF （1258KB）（55）       Knowledge map    Save Network coding allows the intermediate node to encode the received message and forward it to the downstream node. However, there exists the insecurity of transmission data and the polluted information network nodes to waste many network resources. To solve the problems, a network coding hybrid encryption scheme (NCHES) suitable for Internet of things (IoT) system is devised in this article. NCHES realizes secure communication of arbitrary messages and uses a homomorphic hash function to resist the data pollution attacks; it has lower calculation overhead and higher data transmission efficiency in the IoT system. Reference | Related Articles | Metrics

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A Comparative Study on the Legal Liability System for Critical Information Infrastructure#br# Journal of Information Security Reserach    2025, 11 (11): 986-.   Abstract （80）      PDF （1029KB）（26）       Knowledge map    Save A preliminary legal framework for China’s critical information infrastructure (CII) has been established. However, there remain several issues, including the insufficient industryspecificity and adaptability of the legal liability system, the inadequate coordination between accountability mechanisms and the enforcement of penalties, and the need for enhanced alignment with international regulations. By conducting a comparative analysis of the frameworks and models of CII legal liability systems in countries and organizations such as the United States, the European Union, and Russia, it is suggested that China should build a targeted CII liability system, strengthen the coordinated mechanism between law enforcement and accountability, and actively participate in international CII security protection. Reference | Related Articles | Metrics

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Research on Blockchainbased Trusted Manufacturing Traceability System Journal of Information Security Reserach    2025, 11 (11): 1020-.   Abstract （63）      PDF （1823KB）（35）       Knowledge map    Save In view of the issues encountered in product tracking and traceability during the manufacturing process—such as data dispersion, opaque information, susceptibility to tampering, and the lack of trust—this paper proposes a construction method for a trustworthy manufacturing traceability system based on blockchain. First, the system architecture and its key functions are designed, and the system workflow is clearly specified. A production transaction control model based on smart contracts is developed to facilitate interactions between users and producers as well as to control the business processes among different production nodes. Using Cuckoo filters as a priori conditions, an onchain and offchain data storage and retrieval algorithm based on Cuckoo filters and Merkle trees is established to achieve efficient traceability process retrieval. This method stores key information onchain while storing raw data in an offchain database. Finally, system performance is evaluated on a Hyperledger Fabric blockchain network. The results indicate that the retrieval algorithm based on Cuckoo filters and Merkle trees improves system throughput and resource utilization efficiency, while also reducing transaction latency. Reference | Related Articles | Metrics

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An Efficient Detection Method of Phishing Email Based on  Language Model and LoRA Journal of Information Security Reserach    2025, 11 (12): 1117-.   Abstract （101）      PDF （2705KB）（28）       Knowledge map    Save Phishing email detection is critical in cybersecurity, as it faces significant challenges due to the diverse and complex nature of phishing emails. This paper proposes a phishing email detection method integrating the pretrained language model DistilBERT with LowRank Adaptation (LoRA). DistilBERT is used to extract deep semantic features from email text, while LoRA finetunes a small number of parameters, thereby reducing the dependence on largescale labeled data and enhancing the model generalization. Experimental results show that compared to traditional machine learning methods and deep learning models (such as RNN, LSTM, and Bidirectional LSTM), DistilBERT+LoRA outperforms them in key metrics including accuracy, precision, recall, and F1score, achieving 96% accuracy and 97% F1score, which significantly surpassing comparative models. Additionally, it demonstrates better balance between precision and recall than other deep learning models, particularly demonstrating robustness and adaptability in detecting complex phishing emails. Experiments further reveal that the model’s performance improves with the increase in LoRA’s rank parameters. By leveraging the powerful feature extraction capabilities of pretrained language models and the efficient finetuning advantages of LoRA, this method provides an innovative and effective solution for accurate and efficient phishing email detection. Reference | Related Articles | Metrics