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Malicious Client Detection and Defense Method for Federated Learning Journal of Information Security Reserach    2024, 10 (2): 163-.   Abstract （258）      PDF （806KB）（166）       Knowledge map    Save Federated learning allows participating clients to collaborate in training machine learning models without sharing their private data. Since the central server cannot control the behavior of clients, malicious clients may corrupt the global model by sending manipulated local gradient updates, and there may also be unreliable clients with low data quality but some value. To address the above problems, this paper proposes FedMDD,a defense approach for malicious client detection and defense for federated learning, to process detected malicious and unreliable clients in different ways based on local gradient updates, while defending against symbol flipping, additive noise, single label flipping, multilabel flipping, and backdoor attacks. Four baseline algorithms are compared for two datasets, and the experimental results show that FedMDD can successfully defend against various types of attacks in a training environment containing 50% malicious clients and 10% unreliable clients, with better results in both improving model testing accuracy and reducing backdoor accuracy. Related Articles | Metrics

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Research on Privacy Protection Technology in Federated Learning Journal of Information Security Reserach    2024, 10 (3): 194-.   Abstract （221）      PDF （1252KB）（245）       Knowledge map    Save In federated learning, multiple models are trained through parameter coordination without sharing raw data. However,  the extensive parameter exchange in this process renders the model vulnerable to threats not only from external users but also from internal participants. Therefore, research on privacy protection techniques in federated learning is crucial. This paper introduces the current research status on privacy protection in federated learning. It classifies the security threats of federated learning into external attacks and internal attacks.Based on this classification,  it summarizes external attack techniques such as model inversion attacks, external reconstruction attacks, and external inference attacks, as well as internal attack techniques such as poisoning attacks, internal reconstruction attacks, and internal inference attacks. From the perspective of attack and defense correspondence, this paper summarizes data perturbation techniques such as central differential privacy, local differential privacy, and distributed differential privacy, as well as process encryption techniques such as homomorphic encryption, secret sharing, and trusted execution environment. Finally, the paper analyzes the difficulties of federated learning privacy protection technology and identifies the key directions for its improvement. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 236-.   Abstract （209）      PDF （796KB）（198）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 246-.   Abstract （206）      PDF （1562KB）（136）       Knowledge map    Save 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 （194）      PDF （1560KB）（125）       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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Research for Zero Trust Security Model Journal of Information Security Reserach    2024, 10 (10): 886-.   Abstract （193）      PDF （2270KB）（193）       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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Federated Foundation Model Finetuning Based on Differential Privacy#br# #br# Journal of Information Security Reserach    2024, 10 (7): 616-.   Abstract （192）      PDF （1752KB）（129）       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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A Review of Algorithmic Risk and Its Governance in China#br# #br# Journal of Information Security Reserach    2024, 10 (2): 114-.   Abstract （192）      PDF （1781KB）（120）       Knowledge map    Save In the era of digital intelligence, algorithms pervade every corner of human society. While algorithms drive the transformation towards digitization and intelligence, they also give rise to a series of issues, necessitating effective governance of increasing algorithmic risks. Firstly, algorithmic risks are categorized into four fields: law and justice, politics and governance, information dissemination and business and economy. Then the formation mechanisms of algorithmic risk are analyzed, encompassing algorithm black box, algorithm discrimination and power alienation. Finally, a governance strategy framework is proposed, consisting of three paths: technology regulation, power and responsibility normative, and ecological optimization. The research systematically presents the progress and development trend of algorithmic risk and its governance in China, providing reference for advancing the theoretical research and system construction inalgorithmic risk governance. Reference | Related Articles | Metrics

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Security Risks and Countermeasures to Artificial Intelligence#br# #br# Journal of Information Security Reserach    2024, 10 (2): 101-.   Abstract （190）      PDF （469KB）（258）       Knowledge map    Save Related Articles | Metrics

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Malicious TLS Traffic Detection Based on Graph Representation#br# #br# Journal of Information Security Reserach    2024, 10 (3): 209-.   Abstract （187）      PDF （1728KB）（123）       Knowledge map    Save Owing to the need for privacy protection, encryption services online are becoming increasingly popular. However, this also provides an avenue for malicious traffic to hide itself. As a result, the identification of encrypted malicious traffic has become an important task for network management. Currently, some mainstream techniques based on machine learning and deep learning have achieved good results. However, most of these methods ignore the structure of traffic and do not provide indepth analysis of encryption protocols. To address this problem, this paper proposes a graph representation method for SSLTLS traffic, summarizes the key features of TLS traffic and considers traffic correlation from the perspective of multiple attributes such as source IP, destination port and packet count of the flow. Furthermore, this paper establishes a malicious traffic identification framework GCNRF based on graph convolutional neural network and random forest algorithm. This method transforms traffic into graph structure, integrates the structural information and node features of traffic for identification and classification. Experimental results on real public datasets show that the classification accuracy of this method is higher than that of current mainstream models. Reference | Related Articles | Metrics

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A Network Intrusion Detection Model Integrating CNN-BiGRU and  Attention Mechanism Journal of Information Security Reserach    2024, 10 (3): 202-.   Abstract （182）      PDF （2042KB）（171）       Knowledge map    Save To enhance the feature extraction capabilities and classification accuracy of the network intrusion detection model, a network intrusion detection model integrating CNNBiGRU (Convolutional Neural NetworkBidirectional Gated Recurrent Unit) and attention mechanism is proposed. CNN is employed to effectively extract nonlinear features from traffic datasets,while BiGRU extracts timeseries features. The attention mechanism is then integrated to differentiate the importance of different types of traffic data through weighted means, thereby improvingthe overall performance of the model in feature extraction and classification. The experimental results indicate that the overall accuracy rate is 2.25% higher than that of the BiLSTM (Bidirectional Long ShortTerm Memory) model. Kfold crossvalidation results demonstrate that the proposed model's good generalization performance, avoiding the occurrence of overfitting phenomenon, and affirming its effectiveness and rationality. Reference | Related Articles | Metrics

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Legislative Thinking of Artificial Intelligence Law in the Era of  Generative Artificial Intelligence Journal of Information Security Reserach    2024, 10 (2): 103-.   Abstract （167）      PDF （874KB）（142）       Knowledge map    Save With the technological advancements and widespread adoption of Generative Artificial Intelligence (GAI), the structure of human society has undergone fundamental changes.The development of artificial intelligence technology has brought new risks and challenges. The “Interim Measures for the Management of Generative Artificial Intelligence Services” represents China’s latest exploration achievement in the field of GAI. It emphasizes the dual importance of development and security, advocates for innovation and governance in accordance with the law, and serves as a reference and inspiration for the ongoing legislative process of the Artificial Intelligence Law. Specifically, the Artificial Intelligence Law should consider the adoption of promoting legislative model, reduce the use of normative references in the legislative content, clarify the legislative approach of classification and grading, enhance  international exchanges and cooperation in artificial intelligence, and promote the positive use of science and technology by establishing a more scientific and reasonable toplevel design scheme. Reference | Related Articles | Metrics

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Generative Fake Speech Security Issue and Solution#br# #br# Journal of Information Security Reserach    2024, 10 (2): 122-.   Abstract （165）      PDF （1170KB）（134）       Knowledge map    Save The development of generative artificial intelligence algorithms has made the generation of fake speech increasingly natural and fluid, making it challening for human listeners  to distinguish the genuine and fake speech. This paper firstly analyzes a series of threats to society posed by the improper abuse of generative fake speech, including an increase in telecommunication fraud, a decline in the security of voiceoperated applications, judicial fairness of forensic identification, and deception to the public through the combination of falsified information across various domains. Subsequently, the paper summarizes and classifies the algorithms of fake speech generation and fake speech detection technology from the perspective of technology development. We explains the procedural aspects of the technologies and their key points, along with an analysis of the challenges encountered in the process of application. Finally, this paper outlines strategies to prevent and address these security issues from four aspects: technical application, institutional regulation, public education and international cooperation. Reference | Related Articles | Metrics

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Research on Identity Authentication Technology Based on Block Chain and PKI Journal of Information Security Reserach    2024, 10 (2): 148-.   Abstract （165）      PDF （1573KB）（218）       Knowledge map    Save Public key infrastructure (PKI) is a secure system based on asymmetric cryptographic algorithm and digital certificate to realize identity authentication and encrypted communication, operating on the principle of  trust transmission based on trust anchor. However, this technology has the following problems: The CA center is unique and there is a single point of failure; The authentication process involves a large number of operations, such as certificate resolution, signature verification, and certificate chain verification. To solve the above problems, this paper builds an identity authentication model based on Changan Chain, and proposes an identity authentication scheme based on Changan Chain digital certificate and public key infrastructure. Theoretical analysis and experimental data demonstrate  that this scheme reduces certificate parsing, signature verification and other operations, simplifies the authentication process, and improves the authentication efficiency. Reference | Related Articles | Metrics

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Keytarget Face Recognition Scheme Based on Homomorphic  Encryption and Edge Computing Journal of Information Security Reserach    2024, 10 (11): 1004-.   Abstract （165）      PDF （2205KB）（53）       Knowledge map    Save With the promotion of China’s comprehensive national strength and international status, more and more major international events are held in China’s firsttier cities, such as the 31st Chengdu Universiade and the 19th Hangzhou Asian Games. The huge flow of people and complex crowd categories have caused considerable security pressure on the security team. Because the traditional face recognition system realizes face recognition in the central server in plaintext state and relies on the traditional state secret algorithm to ensure security, the computational efficiency and security of the whole system cannot be fully guaranteed. Therefore, based on the CKKS homomorphic encryption scheme and Insightface face recognition algorithm, this paper proposes a keytarget face recognition scheme supporting edge computing. Firstly, the key face features are encrypted by the CKKS homomorphic encryption scheme, and the ciphertext data are distributed to each frontend monitoring device. After that, the frontend monitoring device is responsible for extracting the face features of the scene crowd and calculating the matching degree with the ciphertext database. Finally, the ciphertext calculation results are returned to the central server and decrypted. Experimental results show that the recognition accuracy of the proposed scheme is 98.2116% when the threshold is 1.23 on LFW data sets, which proves the reliability of the proposed scheme. 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 （161）      PDF （2816KB）（133）       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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Reversible Video Information Hiding Based on Multi-pass Motion Vector Ordering Journal of Information Security Reserach    2024, 10 (8): 698-.   Abstract （160）      PDF （1590KB）（91）       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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Model of Intrusion Detection Based on Federated Learning and Convolutional Neural Network Journal of Information Security Reserach    2024, 10 (7): 642-.   Abstract （159）      PDF （1722KB）（134）       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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Multilabel Classification Method of Open Source Threat Intelligence Text Based on BertTextCNN Journal of Information Security Reserach    2024, 10 (8): 760-.   Abstract （148）      PDF （1641KB）（96）       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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Research on Locally Verifiable Aggregate Signature Algorithm Based on SM2 Journal of Information Security Reserach    2024, 10 (2): 156-.   Abstract （145）      PDF （983KB）（135）       Knowledge map    Save The SM2 algorithm is based on the elliptic curve cryptosystem, which was released by the State Cryptography Administration in 2010. At present, it is widely used in egovernment, medical care, finance and other fields. Among them, digital signature is the main application of SM2 algorithm, and the number of signature and verification operations generated in various security application scenarios has increased exponentially. Aiming at the problem that massive SM2 digital signatures occupy a large storage space and the efficiency of verifying signatures one by one is low. This paper proposes a partially verifiable aggregate signature scheme based on the national secret SM2 algorithm, which uses aggregate signatures to reduce storage overhead and improve verification efficiency. On the other hand, when the verifier only needs to verify the specified message and the aggregated signature, it must also obtain the plaintext of all the messages at the time of aggregation. Using partially verifiable signatures, the verifier only needs to specify the message, aggregate signature and short prompt to complete the verification. Analyze the correctness and security of this scheme. Through experimental data and theoretical analysis, compared with similar schemes, this scheme has higher performance. 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 （139）      PDF （2426KB）（183）       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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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 （137）      PDF （1274KB）（155）       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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A Review of Hardware Accelerated Research on Zeroknowledge Proofs Journal of Information Security Reserach    2024, 10 (7): 594-.   Abstract （137）      PDF （1311KB）（127）       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 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 （136）      PDF （1704KB）（114）       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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Research Advance and Challenges of Fuzzing Techniques Journal of Information Security Reserach    2024, 10 (7): 668-.   Abstract （132）      PDF （1020KB）（124）       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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Security Status Analysis and Countermeasures of Basic Software Supply Cha Journal of Information Security Reserach    2024, 10 (8): 780-.   Abstract （130）      PDF （4217KB）（103）       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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A Differential Privacy Text Desensitization Method for Enhancing Semantic Consistency Journal of Information Security Reserach    2024, 10 (8): 706-.   Abstract （126）      PDF （1067KB）（68）       Knowledge map    Save Text desensitization is an extremely important privacy protection method, and the balance between its privacy protection effect and semantic consistency with the original text is a challenge. When existing differential privacy desensitization methods are used to desensitize sensitive words, the similarity calculation probability method is used to select substitute words for sensitive words, which can easily cause inconsistency or even irrelevance between the substitute words and the original text semantics, seriously affecting the preservation of the original text semantics in the desensitized text. A differential privacy text desensitization method is proposed to enhance semantic consistency. A truncation distance measurement formula is given to adjust the probability of selecting replacement words and limit semantic irrelevant replacement words. The experimental results on real datasets show that it effectively improves the semantic consistency between desensitized text and the original text, and has great practical application value. Reference | Related Articles | Metrics

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Research on Location Attack Detection of VANET Based on Incremental Learning Journal of Information Security Reserach    2024, 10 (3): 277-.   Abstract （126）      PDF （1866KB）（92）       Knowledge map    Save In recent years, deep learning has been widely employed in the detection of malicious position attacks on vehicles. However, deep learning models necessitate extensive training time and possess a large number of parameters. Detection methods based on deep learning lack scalability and cannot accommodate the needs of continuously generated new data in vehicular networks. To address these issues, this paper innovatively introduces incremental learning algorithms into the detection of malicious position attacks on vehicles to solve the above problems.This approach first extracts key features from the collected vehicle information data. Subsequently, a malicious position attack detection system is constructed, utilizing ridge regression to quickly approximate the vehicular network’s malicious position attack detection model. Finally, the incremental learning algorithm is applied to update and optimize the malicious position attack detection model to adapt to newly generated data in the vehicular network.Experimental results demonstrate that this method surpasses other methods such as SVM, KNN, and ANN in terms of performance. It can swiftly and progressively update and optimize the old model, thereby enhancing the system’s detection accuracy for malicious position attack behaviors. Reference | Related Articles | Metrics

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Survey of Research on Key Technologies of Internet Content Security Journal of Information Security Reserach    2024, 10 (3): 248-.   Abstract （124）      PDF （1234KB）（116）       Knowledge map    Save The rapid development of the Internet and easy content creation and sharing have made Internet content security a top priority for Internet construction and supervision. The dramatic increase of information content with text, image, audio, and video as carriers has brought great challenges to Internet content security. Internet content security is rich in connotation, and we focused on four key applications including multimedia content filtering, fake information detection, public opinion perception, and data protection. Then, we summarized key technologies and main research work adopted in those applications. Finally, we discussed and prospected key issues of Internet content security in future research. Reference | Related Articles | Metrics

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Research on the Security Architecture of Artificial Intelligence  Computing Infrastructure Journal of Information Security Reserach    2024, 10 (2): 109-.   Abstract （124）      PDF （1146KB）（153）       Knowledge map    Save The artificial intelligence computing infrastructure is a crucial foundation for the development of artificial intelligence. However, due to its diverse attributes, complex nodes, large number of users, and vulnerability of artificial intelligence itself, the construction and operation of artificial intelligence computing infrastructure face severe security challenges. This article analyzes the connotation and security development background of artificial intelligence computing infrastructure, proposes a security architecture for artificial intelligence computing infrastructure from three aspects: strengthening its own security, ensuring operational security, and facilitating security compliance. It puts forward development suggestions aiming to provide methodological ideas for the security construction of artificial intelligence computing infrastructure, offer a basis for selection and use of safe artificial intelligence computing infrastructure, and provide decisionmaking reference for the healthy and sustainable development of the artificial intelligence industry. Reference | Related Articles | Metrics

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Analysis of Security Blind Area of Large LAN#br# #br# Journal of Information Security Reserach    2024, 10 (4): 335-.   Abstract （123）      PDF （784KB）（106）       Knowledge map    Save This paper proposes the concepts of network blind area, asset blind area and security blind area  as they pretain to the security of large local area networks (LAN).  It analyzes the reasons behind the emergence of these three blind area, describes their forms, and points out their impacts on the security of large LAN. This paper proposes a new perspective for solving the security issues associated with large LAN. Reference | Related Articles | Metrics

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Research on Source Code Vulnerability Detection Based on BERT Model Journal of Information Security Reserach    2024, 10 (4): 294-.   Abstract （120）      PDF （3199KB）（168）       Knowledge map    Save Techniques such as code metrics, machine learning, and deep learning are commonly employed in source code vulnerability detection. However, these techniques have problems, such as their inability to retain the syntactic and semantic information of the source code and the requirement of extensive expert knowledge to define vulnerability features. To cope with the problems of existing techniques, this paper proposed a source code vulnerability detection model based on BERT(bidirectional encoder representations from transformers) model. The model splits the source code to be detected into multiple small samples, converted each small sample into the form of approximate natural language, realized the automatic extraction of vulnerability features in the source code through the BERT model, and then trained a vulnerability classifier with good performance to realize the detection of multiple types of vulnerabilities in Python language. The model achieved an average detection accuracy of 99.2%, precision of 97.2%, recall of 96.2%, and an F1 score of 96.7% across various vulnerability types. This represents a performance improvement of 2% to 14% over existing vulnerability detection methods. The experimental results showed that the model was a general, lightweight and scalable vulnerability detection method. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 2-.   Abstract （118）      PDF （1381KB）（103）       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 （114）      PDF （1824KB）（79）       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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Prediction of Industrial Control System Vulnerability Exploitation Relationships Based on Knowledge Graphreasoning Journal of Information Security Reserach    2024, 10 (6): 498-.   Abstract （113）      PDF （1255KB）（87）       Knowledge map    Save With the rapid growth in the number of vulnerabilities in Industrial Control Systems, the time and economic costs required for manual analysis of vulnerability exploitation are constantly increasing, and current reasoning methods have many deficiencies such as insufficient utilization of information and poor interpretability. To address these problems, a prediction method for exploitation relationships of ICS vulnerabilities is proposed, which is based on knowledge graph reasoning. First, a path filtering algorithm is utilized to minimize the vulnerability exploitation paths. Then, path information is obtained by aggregating key relation paths, and neighbor information is acquired by integrating neighbor relation information. Finally, the exploitation relationships of vulnerabilities are predicted. An experiment on predicting exploit relationships was conducted using a knowledge graph for ICS security, which was built based on security knowledge data and ICS scenario data, and consisted of 57333 entities. The results indicate that the proposed method can assist in predicting the exploitability of ICS vulnerabilities with an accuracy rate of 99.0%. 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 （110）      PDF （2800KB）（107）       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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Behavior Conflict Detection Model Based on Transformer and  Graph Convolution Networks Journal of Information Security Reserach    2024, 10 (8): 729-.   Abstract （110）      PDF （1811KB）（80）       Knowledge map    Save In recent years, with the increasing number of surveillance cameras and the rapid development of the Internet, there are more and more surveillance and online videos. The automatic detection of behavior conflict in videos is of great significance to reduce the risk of privacy information leakage caused by human auditing, maintain social order and purify the environment online. To fully extract features of behavior conflict from videos and obtain models with good generalization ability and detection performance, we use I3D (inflated 3D convolutional network) and VGGish to extract multimodal features based on the XDViolence dataset, and propose the behavior conflict detection model based on transformer and graph convolution networks (TGBCDM) for behavior conflict detection. The model contains a Transformer encoder module and a graph convolution module, which can effectively capture the longrange dependencies in videos while paying attention to global and local information of video features. After experimental verification, the model outperforms eight existing methods. Reference | Related Articles | Metrics

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A Survey of Forensic Network Attack Source Traceback Journal of Information Security Reserach    2024, 10 (4): 302-.   Abstract （107）      PDF （1134KB）（107）       Knowledge map    Save The concealment and anonymity of cyber attackers pose significant challenges to the field of network attack traceback. This study provides a comprehensive overview of the current state of research on network attack traceback analysis techniques, focusing on three aspects: traffic, scenarios, and samples. Firstly, with respect to traffic traceback, the paper outlines methods and applications based on log records, packet marking, ICMP tracing, and link testing. Secondly, it categorizes traceback techniques for different scenarios, encompassinganonymous networks, zombie networks, springboards, local area networks, and advanced persistent threat attacks, as well as their applications and limitations in realworld environments. Finally, concerning sample analysis, the paper discusses the progress and application scenarios of static and dynamic traceback analysis in the context of malicious code analysis and attack tracing. Reference | Related Articles | Metrics

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A Secure and Efficient Method of Fully Anonymous Vertical Federated Learning Journal of Information Security Reserach    2024, 10 (6): 506-.   Abstract （106）      PDF （888KB）（98）       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 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 （106）      PDF （2610KB）（98）       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