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Journal of Information Security Reserach    2024, 10 (E1): 236-.   Abstract （383）      PDF （796KB）（306）       Knowledge map    Save Reference | Related Articles | Metrics

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Research for Zero Trust Security Model Journal of Information Security Reserach    2024, 10 (10): 886-.   Abstract （279）      PDF （2270KB）（245）       Knowledge map    Save Zero trust is considered a new security paradigm. From the perspective of security models, this paper reveals the deepening and integration of security models in zero trust architecture, with “identity and data” as the main focus. Zero trust establishes a panoramic control object chain with identity at its core, builds defenseindepth mechanisms around object attributes, functions, and lifecycles, and centrally redirects the flow of information between objects. It integrates information channels to achieve layered protection and finegrained, dynamic access control. Finally, from an attacker’s perspective, it sets up proactive defense mechanisms at key nodes in the information flow path. Since zero trust systems are bound to become highvalue assets, this paper also explores the essential issues of inherent security and resilient service capabilities in zerotrust systems. Through the analysis of the security models embedded in zerotrust and its inherent security, this paper aims to provide a clearer technical development path for the architectural design, technological evolution, and selfprotection of zero trust in its application. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 105-.   Abstract （363）      PDF （929KB）（241）       Knowledge map    Save Reference | Related Articles | Metrics

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A Review of Hardware Accelerated Research on Zeroknowledge Proofs Journal of Information Security Reserach    2024, 10 (7): 594-.   Abstract （237）      PDF （1311KB）（232）       Knowledge map    Save ZeroKnowledge Proofs (ZKP) are cryptographic protocols that allow a prover to demonstrate the correctness of a statement to a verifier without revealing any additional information. This article primarily introduces research on the acceleration of zeroknowledge proofs, with a particular focus on ZKPs based on Quadratic Arithmetic Programs (QAP) and Inner Product Proofs (IPA). Studies have shown that the computational efficiency of zeroknowledge proofs can be significantly improved through hardware acceleration technologies, including the use of GPUs, ASICs, and FPGAs. Firstly, the article introduces the definition and classification of zeroknowledge proofs, as well as the difficulties encountered in its current application. Secondly, this article  discusses in detail the acceleration methods of different hardware systems, their implementation principles, and their performance improvements over traditional CPUs. For example, cuZK and GZKP utilize GPUs to perform Multiscalar Multiplication (MSM) and Number Theoretic Transform (NTT), while PipeZK, PipeMSM, and BSTMSM accelerate these computational processes through ASICs and FPGAs. Additionally, the article mentions applications of zeroknowledge proofs in blockchain for concealing transaction details, such as the private transactions in ZCash. Lastly, the article proposes future research directions, including accelerating more types of ZKPs and applying hardware acceleration to practical scenarios to resolve issues of inefficiency and promote the widespread application of zeroknowledge proof technology. 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 （284）      PDF （1560KB）（227）       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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Intelligent Fuzzy Testing Method Based on Sequence Generative Adversarial Networks Journal of Information Security Reserach    2024, 10 (6): 490-.   Abstract （165）      PDF （2426KB）（215）       Knowledge map    Save The increase in the number of vulnerabilities and the emergence of a large number of highly dangerous vulnerabilities, such as supercritical and highrisk ones, pose great challenges to the state of network security. As a mainstream security testing method, fuzz testing is widely used. Test case generation, as a core step, directly determines the quality of fuzz testing. However, traditional test case generation methods based on pregeneration, random generation, and mutation strategies face bottlenecks such as low coverage, high labor costs, and low quality. Generating highquality, highly available, and comprehensive test cases is a difficult problem in intelligent fuzz testing. To address this issue, this paper proposes an intelligent fuzz testing method based on the sequence generation adversarial network (SeqGAN) model. By combining the idea of reinforcement learning, the test case generation is abstracted as a learning and approximate generation problem for universally applicable variablelength discrete sequence data. Innovatively, a configurable embedding layer is added to the generator part to standardize the generation, and a reward function is designed from the dimensions of authenticity and diversity through dynamic weight adjustment. This ultimately achieves the goal of automatically and intelligently constructing a comprehensive, complete, and usable test case set for flexible and efficient intelligent fuzz testing. This paper verifies the proposed scheme from two aspects of effectiveness and universality. The average test case pass rate of over 95% and the average target defect detection rate of 10% under four different testing targets fully demonstrate the universality of the scheme. The 98% test case pass rate, 9% target defect detection rate, and the ability to generate 20000 usable test cases per unit time under four different schemes fully demonstrate the effectiveness of the scheme. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 246-.   Abstract （339）      PDF （1562KB）（209）       Knowledge map    Save 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 （342）      PDF （1752KB）（198）       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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Journal of Information Security Reserach    2024, 10 (E2): 24-.   Abstract （147）      PDF （555KB）（194）       Knowledge map    Save Reference | Related Articles | Metrics

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A Review of GPU Acceleration Technology for Deep Learning in Plaintext  and Private Computing Environments Journal of Information Security Reserach    2024, 10 (7): 586-.   Abstract （188）      PDF （1274KB）（188）       Knowledge map    Save With the continuous development of deep learning technology, the training time of neural network models is getting longer and longer, and using GPU computing to accelerate neural network training has increasingly become a key technology. In addition, the importance of data privacy has also promoted the development of private computing technology. This article first introduces the concepts of deep learning, GPU computing, and two privacy computing technologies, secure multiparty computing and homomorphic encryption, and then discusses the GPU acceleration technology of deep learning in plaintext environment and private computing environment. In the plaintext environment, the two basic deep learning parallel training modes of data parallelism and model parallelism are introduced, two different memory optimization technologies of recalculation and video memory swapping are analyzed, and gradient compression in the training process of distributed neural network is introduced. technology. This paper introduces two deep learning GPU acceleration techniques: Secure multiparty computation and homomorphic encryption in a privacy computing environment. Finally, the similarities and differences of GPUaccelerated deep learning methods in the two environments are briefly analyzed. Reference | Related Articles | Metrics

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Model of Intrusion Detection Based on Federated Learning and Convolutional Neural Network Journal of Information Security Reserach    2024, 10 (7): 642-.   Abstract （205）      PDF （1722KB）（180）       Knowledge map    Save The cyber intrusion detection model needs to identify the malicious data timely and accurately among the largescale cyber traffic data. However, due to the insufficient label data of a single institution and the unwillingness of various institutions to share data, the performance of the trained cyber intrusion detection model has low performance. In view of the above problems, this paper proposed an intrusion detection model FL1DCNN, which combined federated learning and onedimensional convolutional neural network. While ensuring high detection accuracy, it allowed more participants to protect their data privacy and security, which solved the problem of insufficiency of the labeled data. The FL1DCNN model first carried on a series of preprocessing operations on the original data set, then used the onedimensional convolutional neural network as the general model of each participant to extract features under the federated learning mechanism and finally performs binary classification using a sigmoid classifier. The experimental results show that the accuracy of the FL1DCNN model on the CICIDS2017 dataset is 96.5% and the F1score of the FL1DCNN model is 97.9%. In addition, compared to the traditional centralized training model 1DCNN, the FL1DCNN model reduces training time by 32.7%. Reference | Related Articles | Metrics

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Research Advance and Challenges of Fuzzing Techniques Journal of Information Security Reserach    2024, 10 (7): 668-.   Abstract （206）      PDF （1020KB）（173）       Knowledge map    Save Fuzzing. as an efficient vulnerability discovery technique, has garnered increasing attention from researchers due to its rapid development in recent years. To delve deeper into fuzzing techniques, this paper introduces its definition and analyzes the advantages and disadvantages. It summarizes the research progress of fuzzing techniques from various perspectives, including energy scheduling for seed selection, test case mutation algorithms, fuzzy test execution performance, mixed fuzzy testing. Furthermore, it compares the improvement points and shortcomings of different fuzzing studies, and further proposes suggestions for future development. Additionally, the paper describes the research achievements of fuzzing in vulnerability discovery in the fields of operating system kernel, network protocol, firmware, and deep learning. Finally the paper concludes with a summary and offers insights into the future challenges and research hotspots of fuzzing. 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 （193）      PDF （2816KB）（168）       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 Federated Learning Privacy Protection Method for Multikey Homomorphic  Encryption in the Internet of Things Journal of Information Security Reserach    2024, 10 (10): 958-.   Abstract （226）      PDF （1704KB）（166）       Knowledge map    Save With federated learning, multiple distributed IoT devices can jointly train a global model by updating the transmission model without leaking raw data. However, federated learning systems are susceptible to model inference attacks, resulting in compromised system robustness and data privacy. A federated learning privacy protection method for multikey homomorphic encryption in the Internet of Things is proposed to address the issues of existing federated learning solutions being unable to protect the confidentiality of shared gradients and resisting collusion attacks initiated by clients and servers. This method utilizes multikey homomorphic encryption to achieve gradient update confidentiality protection. Firstly, by using proxy reencryption technology, the ciphertext under different public keys is converted into encrypted data under the public key, ensuring that the cloud server can decrypt the gradient ciphertext. Then, IoT devices use their own public key and random secret factor to encrypt local gradient data, which can resist collusion attacks initiated by malicious devices and servers. Secondly, an identity authentication method based on hybrid cryptography was designed to achieve realtime verification of the identities of participants in federated modeling. In addition, in order to further reduce client computing costs, some decryption calculations are coordinated with trusted servers for computation, and users only need a small amount of computation. A comprehensive analysis was conducted on the proposed solution to evaluate its safety and efficiency. The results indicate that the proposed scheme meets the expected security requirements. Experimental simulation shows that compared to existing schemes, this scheme has lower computational overhead and can achieve faster and more accurate model training. Reference | Related Articles | Metrics

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Multifamily Malicious Domain Intrusion Detection Based on #br# Collaborative Attention#br# Journal of Information Security Reserach    2024, 10 (12): 115-.   Abstract （161）      PDF （1317KB）（161）       Knowledge map    Save The timely and accurate detection of illegal domain names can effectively prevent the information loss caused by server crashes or unauthorized intrusions. A multifamily malicious domain name intrusion detection method based on collaborative attention is proposed. Firstly, the deep autoencoder network is used to encode and compress layer by layer, extracting the domain name encoding features at the intermediate layer. Secondly, the longdistance and shortdistance encoding features of the domain name string are extracted from the temporal and spatial dimensions, and the selfattention mechanism is constructed on the temporal and spatial encoding feature maps to enhance the expressiveness of the encoding features in local space. Thirdly, the crossattention mechanism is used to establish information interaction between the temporal and spatial encoding features, enhancing the expressiveness of different dimension encoding features in the global space. Finally, the softmax function is used to predict the probability of the domain name to be tested, and quickly determine the legitimacy of the domain name according to the probability value. The results of testing on multiple families of malicious domain name datasets show that the proposed method can achieve a detection accuracy of 0.9876 in the binary classification task of normal and malicious domain names, and an average recognition accuracy of 0.9568 on 16 family datasets. Compared with other classic methods of the same kind, the proposed method achieves the best detection results on multiple evaluation metrics. Reference | 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 （171）      PDF （1420KB）（154）       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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Research on Data Security Sharing Technology Based on Blockchain and  Proxy Re-encryption Journal of Information Security Reserach    2024, 10 (8): 719-.   Abstract （165）      PDF （2800KB）（149）       Knowledge map    Save In the digital age, a vast amount of sensitive data is stored across various networks and cloud platforms, making data protection a crucial challenge in the field of information security. Traditional encryption methods are vulnerable due to single point of failure and centralized control, which can lead to data leakage. To address these issues, this study proposes a new method that integrates blockchain technology with an improved proxy reencryption algorithm, utilizing Shamir threshold key sharing. A data sharing scheme TDPRBC based on the threshold proxy reencryption algorithm is designed. Security analysis and experimental results show that this scheme can meet most data access needs. Reference | Related Articles | Metrics

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A Large Language Model Detection System for Domainspecific Jargon Ji Xu, Zhang Jianyi, Zhao Zhangchi, Zhou Ziyin, Li Yilong, and Sun Zezheng Journal of Information Security Reserach    2024, 10 (9): 795-.   Abstract （160）      PDF （2610KB）（148）       Knowledge map    Save Large language model (LLM) retrieve knowledge from their own structures and reasoning processes to generate responses to user queries, thus many researchers begin to evaluate the reasoning capabilities of large language models. However, while these models have demonstrated strong reasoning and comprehension skills in generic language tasks, there remains a need to evaluate their proficiency in addressing specific domainrelated problems, such as those found in telecommunications fraud. In response to this challenge, this paper presents the first evaluation system for assessing the reasoning abilities of DomainSpecific Jargon and proposes the first domain specific jargon dataset. To address issues related to cross matching problem and complex data calculation problem, we propose the collaborative harmony algorithm and the data aware algorithm based on indicator functions. These algorithms provide a multidimensional assessment of the performance of large language models. Our experimental results demonstrate that our system is adaptable in evaluating the accuracy of questionanswering by large language models within specialized domains. Moreover, our findings reveal, for the first time, variations in recognition accuracy based on question style and contextual cues utilized by the models. In conclusion, our system serves as an objective auditing tool to enhance the reliability and security of large language models, particularly when applied to specialized domains. Reference | Related Articles | Metrics

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Research on Security Risk and Governance Path of Large Models Journal of Information Security Reserach    2024, 10 (10): 975-.   Abstract （125）      PDF （1104KB）（148）       Knowledge map    Save Reference | Related Articles | Metrics

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Using Artificial Intelligence to Drive Quality and Upgrading of Opensource Big Data Analysis Work Journal of Information Security Reserach    2024, 10 (5): 390-.   Abstract （90）      PDF （1504KB）（144）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Risk Analysis of Opensource Software Supply Chain Security Journal of Information Security Reserach    2024, 10 (9): 862-.   Abstract （189）      PDF （1824KB）（143）       Knowledge map    Save Opensource software has become one of the most fundamental elements that support the operation of the digital society. It has also been penetrated to various industries and fields. As the opensource software supply chain becomes increasingly complex and diversified, the risks caused by security attacks on the opensource software supply chain are also intensified. This paper summarizes the current development of the opensource software supply chain ecosystem and the strategic layout of opensource software supply chain security in major countries. From the dimensions of development security, usage security, and operation security, this paper proposes an opensource software supply chain security risk analysis system. It identifies the major security risks currently faced by the opensource software supply chain. Besides, this paper constructs a security assurance model for the opensource software supply chain and offers countermeasures and suggestions for the security and development of China’s opensource software supply chain from the dimensions of supply chain phases, relevant entities, and safeguard measures. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 7-.   Abstract （153）      PDF （1223KB）（141）       Knowledge map    Save Reference | Related Articles | Metrics

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An Optimized Computation Method for Cipher Symbol Functions  Based on Homomorphic Encryption Journal of Information Security Reserach    2025, 11 (2): 100-.   Abstract （165）      PDF （1092KB）（139）       Knowledge map    Save Fully homomorphic encryption extends encryption to computations, allowing ciphertext processing without decryption. Comparative operations, crucial in applications like deep learning, pose a challenge in homomorphic encryption environments restricted to addition and multiplication. Feng et al. (CNS 2023) proposed a comparison method using dynamic polynomial combinations. This paper enhances dynamic polynomial, allowing polynomial fluctuations within (-2,2). It introduces a novel equation system for solving dynamic polynomials and utilizes finite third and fifthdegree polynomials to construct more precise composite polynomials for approximating the sign function. It analyzes the method’s optimality in depth consumption and computational complexity, achieving a 32% reduction in runtime compared to the optimal method in a previous study (CNS 2023). The homomorphic comparison algorithm in this paper, for ε=2-20，α=20 requires only 0.69ms in amortized runtime. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 54-.   Abstract （113）      PDF （1425KB）（138）       Knowledge map    Save Reference | Related Articles | Metrics

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Multilabel Classification Method of Open Source Threat Intelligence Text Based on BertTextCNN Journal of Information Security Reserach    2024, 10 (8): 760-.   Abstract （226）      PDF （1641KB）（138）       Knowledge map    Save Open source threat intelligence is very important for network security protection, but it has the characteristics of wide distribution, many forms and loud noise. Therefore, how to organize and analyze the collected massive open source threat intelligence efficiently has become an urgent problem to be solved. Therefore, this paper explores a multilabel classification method based on BertTextCNN model, considering the title, text, and regular judgment. According to the characteristics of the text published by the intelligence source, the article sets regular judgment rules to make up for the deficiency of the model. In order to fully reflect the threat topics involved in the open source threat intelligence text, the paper sets the BertTextCNN multilabel classification model for the title and the text respectively, and then resorts the two labels to get the final threat category of the text. Compared with the BertTextCNN multilabel classification model based on text only, the performance of the proposed model is improved, and the recall rate is significantly improved, which can provide valuable reference for the classification of open source threat intelligence. Reference | Related Articles | Metrics

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Security Status Analysis and Countermeasures of Basic Software Supply Cha Journal of Information Security Reserach    2024, 10 (8): 780-.   Abstract （162）      PDF （4217KB）（137）       Knowledge map    Save Basic software is the cornerstone of supporting the efficient and stable operation of computer systems, which determines the level of development of digital infrastructure. The industrial chain of basic software, represented by operating system, database and middleware, occupies an upstream position in the entire software industry, which directly determines the scale and the efficiency of the downstream output. Due to the characteristics of long R&D cycle and large R&D investment, basic software has gradually attracted attention from various countries and risen to the level of national strategy in the increasingly complex environment of software supply chain. In recent years, while China’s basic software industry has developed rapidly with the help of open source, many security incidents of basic software supply chain have occurred, which brings risks and challenges. This paper reviews the current situation of the basic software supply chain security, analyzes the risks and challenges faced by the basic software supply chain, and puts forward reasonable suggestions from four aspects: policy, industry, user and ecology. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 2-.   Abstract （144）      PDF （1381KB）（135）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Deep Learningbased Spatiotemporal Feature Fusion  Network Intrusion Detection Model Journal of Information Security Reserach    2025, 11 (2): 122-.   Abstract （119）      PDF （1944KB）（134）       Knowledge map    Save As the number of network attacks increases, network intrusion detection systems are becoming increasingly important in maintaining network security. Most studies have used deep learning approaches for network intrusion detection but have not fully utilized the features of traffic from multiple perspectives. Additionally, these studies often suffer from the use of outdated experimental datasets. In this paper, a parallelstructured DSCInceptionBiLSTM network is proposed to evaluate the designed network model using stateoftheart datasets. The model consists of two branches, network traffic image, and text anomaly traffic detection. Spatial and temporal features of traffic are extracted by improved convolutional neural networks and recurrent neural networks, respectively. Finally, network intrusion detection is achieved by fusing spatiotemporal features. The experimental results show that our model achieves 99.96%, 99.19%, and 99.95% accuracy on the three datasets of CICIDS 2017, CSECICIDS 2018 and CICDDoS 2019, respectively, effectively classifying the anomalous traffic with high precision and meeting the requirements of intrusion detection system. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 27-.   Abstract （205）      PDF （763KB）（131）       Knowledge map    Save Reference | Related Articles | Metrics

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Research and Practice of 5G Network Security Assessment Technologies Journal of Information Security Reserach    2024, 10 (6): 539-.   Abstract （75）      PDF （5554KB）（131）       Knowledge map    Save With the widespread deployment of 5G networks, establishing effective network security assessment mechanisms has become increasingly important to ensure network safety and mitigate risks. This paper proposes a standardized 5G network security assessment process to address the security risks arising from the complexity of 5G technology, the iterative nature of standards, and the diversity of applications. The approach includes an integrated suite of technical solutions such as a digital twinbased 5G security parallel simulation testing platform, security penetration techniques based on the ATT&CK model, and 5G security fuzz testing. These solutions have been incorporated into a practical 5G security evaluation framework and validated through realworld case studies. The results demonstrate that the proposed assessment process and technologies effectively address emerging security challenges and enhance the overall security of 5G networks. Reference | Related Articles | Metrics

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Multiuser Searchable Symmetric Encryption Scheme Based on  Elliptic Curve Encryption Journal of Information Security Reserach    2024, 10 (7): 624-.   Abstract （105）      PDF （1306KB）（125）       Knowledge map    Save Searchable encryption (SE) is one of the key technologies in secure data retrieval. It allows the server to search encrypted data directly without decrypting it. In this paper, we propose a multiuser extension of the existed dynamic searchable symmetric encryption (SSE) scheme for the singleuser to solve the problem of data security sharing in cloud storage environment. The proposed scheme is efficient, secure and requires no storage on the client. The scheme uses elliptic curve encryption system to realize key management and access key distribution among authorized users，effectively avoiding the key sharing problem and bilinear pairing operation in traditional multiuser scheme. It also meets the requirements of query privacy, search unforgeability, and user revocability. At the same time, after multiuser expansion, the scheme still maintains the advantages of the original scheme, such as less information leakage, efficient file search, efficient file deletion and no storage on the client. Reference | Related Articles | Metrics

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A Secure and Efficient Method of Fully Anonymous Vertical Federated Learning Journal of Information Security Reserach    2024, 10 (6): 506-.   Abstract （137）      PDF （888KB）（124）       Knowledge map    Save As a key technical paradigm to achieve “data availability and invisibility”, the core process of vertical federated learning is sample alignment based on private set intersection. Although the private set intersection protects the privacy of nonintersected information, it can’t meet the privacy protection requirements of user IDs in the intersected set. This paper proposes a fully anonymous vertical federated learning framework based on anonymous alignment to ensure that no private information of each holder set will be disclosed during the whole process. An implementation framework based on secure multiparty computation is proposed for fully anonymous joint modeling. The high performance and low error characteristics of the framework are verified through experiments, indicating it can be better applied in practice. Reference | Related Articles | Metrics

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A Retrospective and Future Development Study of Zero Trust Architecture Journal of Information Security Reserach    2024, 10 (10): 896-.   Abstract （118）      PDF （1683KB）（124）       Knowledge map    Save With the rapid development of the internet, big data, and cloud computing, the zero trust architecture has been proposed as a new security paradigm to address the challenges of modern digitalization. This security model is built on never inherently trusting any internal or external requests, emphasizing that access must be granted through constant verification and monitoring. The core principles of zero trust include comprehensive identity verification, access control, least privilege, pervasive encryption, and continuous risk assessment and response. This article primarily reviews the development history of zero trust architecture, elaborates on the basic concepts of the zero zrust mechanism, and finally summarizes the future development of zero trust architecture. Reference | Related Articles | Metrics

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Reversible Video Information Hiding Based on Multi-pass Motion Vector Ordering Journal of Information Security Reserach    2024, 10 (8): 698-.   Abstract （214）      PDF （1590KB）（122）       Knowledge map    Save Aiming at the problem that existing reversible video information hiding algorithms based on motion vector ordering cannot adaptively adjust the embedding capacity according to the visual characteristics of video frames and have limited capacity, a multipass vector ordering reversible video information hiding algorithm is proposed. This algorithm decides whether to embed information in subsequent frames by assessing the texture and motion complexities of reference frames, thereby enabling adaptive information embedding in subsequent frames. The algorithm also enhances the multipass pixel value ordering (multipass PVO) technique and applies it to video information hiding, significantly enhancing the embedding capacity of reversible hiding algorithms. Experimental results demonstrate that, compared to similar algorithms, the variation values of PSNR and SSIM decreased by 14.5% and 8.5% respectively, and the embedding capacity increased by 7.4%. This represents significant improvements in both visual quality and embedding capacity. Reference | Related Articles | Metrics

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Research on Risk Analysis and Countermeasures of Software Supply  Chain in Critical Information Infrastructure Journal of Information Security Reserach    2024, 10 (9): 833-.   Abstract （153）      PDF （1295KB）（120）       Knowledge map    Save System security protection is crucial to critical information infrastructures (CII), and  software supply chain risk analysis is indispensable. In recent years, the number of supply chain attack incidents has increased rapidly. This paper first analysis the potential problems of “external” software components, personnel, tools, etc., which are the main causes of software supply chain threats, and then summarize the current research of domestic and foreign policies and technologies. Based on these findings, a software supply chain security framework for power industry systems is proposed. It covers 15 groups of security measures across 4 aspects, including external component governance, supplier management, development and operation facilities reinforcement, usage mechanism of the software bill of materials (SBOM), all of which can be  further extended. This framework can provide references on software supply chain security protection in power industry information systems. Reference | Related Articles | Metrics

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A DNS Root Zone Management Architecture Based on Consortium Blockchain Journal of Information Security Reserach    2024, 10 (7): 602-.   Abstract （88）      PDF （2981KB）（120）       Knowledge map    Save The centralized root architecture of Domain Name System (DNS) in the current Internet is accompanied by longterm concerns: on one hand, the country code toplevel domain may be out of control due to the destruction of the root authority function; on the other hand, it is worried that decentralized root alternatives will cause the domain name space to split. The root cause of the above concerns lies in the lack of autonomy and transparency in current and alternative root zone management, leading to a lack of trust in the current root authority or alternative solutions. This paper describes a new DNS root zone management architecture, the root consensus chain, to enhance mutual trust and ease the concerns of all parties. Multiple autonomous registries participate in root zone management in the root consensus chain. Each registry has a country code toplevel domain and root server operators to jointly build a consortium blockchainbased root zone management system. While maintaining a unified name space and a unique global root authority, the root consensus chain improves autonomy through the establishment of a root community by the root consensus chain managers; improves transparency by recording and executing the agreements among the parties and the operation of the root zone. The experimental results based on the real network research testbed show that the root consensus chain can effectively cope with the above concerns, and it has good feasibility and practicability. Reference | Related Articles | Metrics

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Research on Science and Technology Management Information Security Guarantee System Journal of Information Security Reserach    2024, 10 (7): 675-.   Abstract （74）      PDF （1766KB）（119）       Knowledge map    Save Technological security is a crucial component of the national security framework, serving as a vital force underpinning national security. To strengthen technological innovation and ensure technological security, it is imperative to establish a comprehensive and efficient national innovation system. The information security aspect of technology management information systems, as a pivotal lever for deepening technological institutional reform, should not be underestimated. This paper begins by introducing the significance of technology management, technological security, and the maintenance of information security in technology management. It also highlights the current risk challenges faced in technology management information security. Then, combined with technology management information security protection needs, it constructs a security assurance framework for technology management information security. Detailed explanations are provided on the security management system, security operations and maintenance system, and security technology system within this architecture, with a particular focus on strategies for safeguarding the security of technology management business data. Finally, an analysis is presented regarding the development trends in technology management information security assurance. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 45-.   Abstract （120）      PDF （820KB）（119）       Knowledge map    Save Reference | Related Articles | Metrics

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Traffic Anomaly Detection Method by Secondorder Feature  Journal of Information Security Reserach    2024, 10 (12): 1082-.   Abstract （103）      PDF （2415KB）（119）       Knowledge map    Save A method is proposed to address the challenge of low detection rates for minority class attack traffic in deep learning models when dealing with imbalanced massive highdimensional network traffic data. Firstly, the isolation forest (iForest) is employed to remove outliers from normal class samples, used for training an enhanced Convolutional Denoising Autoencoder (CDAE) to mitigate the impact of noise and outliers on model training, resulting in a lowdimensional enhanced representation of the original features. Secondly, leveraging ADASYN on the outlierfree dataset to synthetically generate minority class attack samples, thereby resolving the data imbalance issue. Subsequently, using iForest to clean the newly generated samples from outliers, a new dataset is obtained. Employing the pretrained CDAE on this dataset achieves a firstround feature extraction, and the extracted features serve as input for a selfdistilled ResNet model to perform secondorder feature extraction. Finally, precise identification of anomalous traffic is accomplished by combining the trained CDAE and ResNet models. The method achieves the highest fiveclass accuracy and F1 score of 91.52% and 92.05%, respectively, on the NSLKDD dataset. Experimental results demonstrate that, compared to existing methods, this approach effectively enhances the detection rates for minority class attack traffic. Reference | Related Articles | Metrics

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An Adaptive Network Attack Analysis Method Based on Federated Learning Journal of Information Security Reserach    2024, 10 (12): 1091-.   Abstract （99）      PDF （3389KB）（116）       Knowledge map    Save To analyze network attack behavior issues efficiently and securely, an adaptive network attack analysis method based on federated learning (NAAFL) is proposed. This approach can fully leverage data for network attack analysis while ensuring privacy protection.. Firstly, a costeffective defense mechanism based on DQN (dynamic participant selection mechanism) is proposed to act in the process of federated learning model parameter sharing and model aggregation. It dynamically selects the best participants for each round of model updates, reducing the impact of poorly performing local models on the global model during training. It also reduces communication overhead time and improving the efficiency of federated learning. Secondly, an adaptive feature learning network intrusion detection model is designed, which is able to intelligently learn and analyze according to changing attack features to cope with complex network environments. It effectively reduces the time and space overhead of feature selection. Finally, comparative experiment is performed on a public data set (NSL KDD). The NAAFL method detects attacks with an accuracy of 98.9%. Dynamically selecting participants increases server accuracy by 4.48%. The experimental results show that the method has excellent robustness and efficiency. Reference | Related Articles | Metrics