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Journal of Information Security Reserach    2023, 9 (E2): 4-.   Abstract （99）      PDF （2945KB）（906）       Knowledge map    Save Related Articles | Metrics

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Journal of Information Security Reserach    2023, 9 (E1): 105-.   Abstract （651）      PDF （1450KB）（374）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 236-.   Abstract （458）      PDF （796KB）（351）       Knowledge map    Save 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 （258）      PDF （469KB）（339）       Knowledge map    Save Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 105-.   Abstract （489）      PDF （929KB）（316）       Knowledge map    Save Reference | 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 （447）      PDF （1252KB）（314）       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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Research for Zero Trust Security Model Journal of Information Security Reserach    2024, 10 (10): 886-.   Abstract （326）      PDF （2270KB）（287）       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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Key Technologies and Research Prospects of Privacy Computing Journal of Information Security Reserach    2023, 9 (8): 714-.   Abstract （375）      PDF （1814KB）（281）       Knowledge map    Save Privacy computing, as an important technical means taking into account both data circulation and privacy protection, can effectively break the “data island” barriers while ensuring data security, it enables open data sharing, and promotes the deep mining and use of data and crossdomain integration. In this paper, the background knowledge, basic concepts and architecture of privacy computing were introduced, the basic concepts of three key technologies of privacy computing, including secure multiparty computation, federated learning and trusted execution environment were elaborated, and studies on the existing privacy security was conducted, a multidimensional comparison and summarization of the differences of the three key technologies were made. On this basis, the future research direction of privacy computing is prospected from the technical integration of privacy computing with blockchain, deep learning and knowledge graph. Reference | Related Articles | Metrics

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Malicious Client Detection and Defense Method for Federated Learning Journal of Information Security Reserach    2024, 10 (2): 163-.   Abstract （867）      PDF （806KB）（279）       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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A Review of Adversarial Attack on Autonomous Driving Perception System Journal of Information Security Reserach    2024, 10 (9): 786-.   Abstract （360）      PDF （1560KB）（277）       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 on Identity Authentication Technology Based on Block Chain and PKI Journal of Information Security Reserach    2024, 10 (2): 148-.   Abstract （260）      PDF （1573KB）（270）       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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Journal of Information Security Reserach    2024, 10 (E1): 246-.   Abstract （421）      PDF （1562KB）（270）       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 （577）      PDF （1311KB）（267）       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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Survey of Intelligent Vulnerability Mining and Cyberspace Threat Detection Journal of Information Security Reserach    2023, 9 (10): 932-.   Abstract （342）      PDF （1093KB）（263）       Knowledge map    Save At present, the threat of cyberspace is becoming more and more serious. A large number of studies have focused on cyberspace security defense techniques and systems. Vulnerability mining technique can be applied to detect and repair vulnerabilities in time before the occurrence of network attacks, reducing the risk of intrusion; while threat detection technique can be applied to threat detection during and after network attacks occur, which can detect threats in a timely manner and respond to them, reducing the harm and loss caused by intrusion. This paper analyzed and summarized the research on vulnerability mining and cyberspace threat detection based on intelligent methods. In the aspect of intelligent vulnerability mining, the current research progress is summarized from several application classifications combined with artificial intelligence technique, namely vulnerability patch identification, vulnerability prediction, code comparison and fuzz testing. In the aspect of cyberspace threat detection, the current research progress is summarized from the classification of information carriers involved in threat detection based on network traffic, host data, malicious files, and network threat intelligence. Reference | Related Articles | Metrics

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Android Malware Multiclassification Model Based on Transformer Journal of Information Security Reserach    2023, 9 (12): 1138-.   Abstract （259）      PDF （2073KB）（259）       Knowledge map    Save Due to the open source and openness, the Android system has become a popular target for malware attacks, and there are currently a large number of research on Android malware detection, among which machine learning algorithms are widely used. In this paper, the Transformer algorithm is used to classify and detect the grayscale images converted by Android software classes.dex files, and the accuracy rate reaches 86%, which is higher than that of CNN, MLP and other models. 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 （266）      PDF （3199KB）（257）       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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The Status and Trends of Confidential Computing Journal of Information Security Reserach    2024, 10 (1): 2-.   Abstract （286）      PDF （1466KB）（254）       Knowledge map    Save Related Articles | Metrics

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Classification and Grading Method of Transportation Government Data Journal of Information Security Reserach    2023, 9 (8): 808-.   Abstract （250）      PDF （1008KB）（247）       Knowledge map    Save In order to promote the open sharing of government data and improve data security, it is urgent to solve the classification and grading of government data resources. This paper summarized the experience of domestic and foreign government data classification and grading, using a hybrid classification method combining surface and line to build transportation government data classification framework. A fivelevel data grading model was formed base on the data grading method of data security risk analysis, and the effect of the method was verified by introducing actual data. Transportation government data classification and grading method can effectively assist the relevant departments to carry out classification and grading of government data, as well as important data protection, and promoting the level of industry data security governance and security technology advancement. 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 （407）      PDF （1752KB）（234）       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 （209）      PDF （555KB）（232）       Knowledge map    Save 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 （290）      PDF （2042KB）（231）       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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Intelligent Fuzzy Testing Method Based on Sequence Generative Adversarial Networks Journal of Information Security Reserach    2024, 10 (6): 490-.   Abstract （188）      PDF （2426KB）（226）       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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Research on the Security Architecture of Artificial Intelligence  Computing Infrastructure Journal of Information Security Reserach    2024, 10 (2): 109-.   Abstract （188）      PDF （1146KB）（224）       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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Legislative Thinking of Artificial Intelligence Law in the Era of  Generative Artificial Intelligence Journal of Information Security Reserach    2024, 10 (2): 103-.   Abstract （244）      PDF （874KB）（217）       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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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 （255）      PDF （1274KB）（212）       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 （266）      PDF （1722KB）（207）       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 on Security Risks and Protection of Container Images Journal of Information Security Reserach    2023, 9 (8): 792-.   Abstract （167）      PDF （1788KB）（205）       Knowledge map    Save As the digital transformation speeds up, more and more enterprises shift to adopt container technology to improve business productivity and scalability in order to deepen the process of industrial digital transformation. As the basis for container operation, container images contain packaged applications and their dependencies, as well as process information for container instantiation. However, container images also have various insecure factors. In order to solve the problem from the source and reduce the various security risks and threats faced by containers after they are instantiated, the fulllifecycle management of container images should be implemented. In this paper, the advantages that container images bring to the application development and deployment were investigatesd, the security risks faced by container images were analyzed. Key technologies for container mirroring security protection from the three stages of construction, distribution, and operation were proposed, and then a container image security scanning tool was developed, which can scan container images for applications and underlying infrastructure that use container technology. It was proved to have good practical effects, which can help enterprises achieve fulllifecycle image security protection. 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 （290）      PDF （1704KB）（203）       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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A Survey of Forensic Network Attack Source Traceback Journal of Information Security Reserach    2024, 10 (4): 302-.   Abstract （178）      PDF （1134KB）（201）       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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Research Advance and Challenges of Fuzzing Techniques Journal of Information Security Reserach    2024, 10 (7): 668-.   Abstract （245）      PDF （1020KB）（200）       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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A Review of Algorithmic Risk and Its Governance in China#br# #br# Journal of Information Security Reserach    2024, 10 (2): 114-.   Abstract （322）      PDF （1781KB）（199）       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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Research on Network Malicious Traffic Detection Technology Based on  Ensemble Learning Strategy Journal of Information Security Reserach    2023, 9 (8): 730-.   Abstract （207）      PDF （2586KB）（199）       Knowledge map    Save Network traffic is the main carrier of network attacks, and the identification and analysis of malicious traffic is an important means to ensure network security. Machine learning method has been widely used in malicious traffic identification, which can achieve high precision identification. In the existing methods, the fusion model is more accurate than the single statistical model, but the depth of network behavior mining is insufficient. This paper proposes a stacking model that identifies multilevel network features and is MultiStacking for malicious traffic. It employs the network behavior patterns of network traffic in different session granularity and combines the robust fitting capability of the stacking model for multidimensional data to deeply heap malicious network behaviors. By verifying the detection capabilities of multiple fusion models on the CICIDS2017 and CICIDS2018 datasets, various detection methods are comprehensively quantified and compared, and the performance of MultiStacking detection methods in MultiStacking scenarios is deeply analyzed. The experimental results show that the malicious traffic detection method based on multilevel stacking can further improve the detection accuracy. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2023, 9 (E2): 13-.   Abstract （150）      PDF （1022KB）（198）       Knowledge map    Save 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 （145）      PDF （1104KB）（196）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Malicious Location Attack Detection of VANET Based on  Federated Learning Journal of Information Security Reserach    2023, 9 (8): 754-.   Abstract （258）      PDF （2613KB）（194）       Knowledge map    Save Malicious behavior detection is an important part of the security needs of the Internet of vehicles. In the Internet of vehicles, malicious vehicles can achieve malicious location attack by forging false basic security information (BSM) information. At present, the traditional solution to the malicious location attack on the Internet of vehicles is to detect the malicious behavior of vehicles through machine learning or deep learning. These methods require data collecting, causing privacy problems. In order to solve this problems, this paper proposed a detection scheme of malicious location attacks on the Internet of vehicles based on Federated learning. The scheme does not need to collect user data, and the detection model uses local data and simulated data for local training, which ensures the privacy of vehicle users, reduces data transmission and saves bandwidth. The malicious location attack detection model based on Federated learning was trained and tested using the public VeReMi data set, and the performance of the data centric malicious location attack detection scheme was compared. Through comparison, the performance of malicious location attack detection based on Federated learning is similar to that of traditional data centric malicious location attack detection scheme, but the malicious location attack detection scheme based on Federated learning is better in data transmission and privacy protection. 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 （205）      PDF （2816KB）（193）       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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Survey of Research on Key Technologies of Internet Content Security Journal of Information Security Reserach    2024, 10 (3): 248-.   Abstract （235）      PDF （1234KB）（189）       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 Locally Verifiable Aggregate Signature Algorithm Based on SM2 Journal of Information Security Reserach    2024, 10 (2): 156-.   Abstract （296）      PDF （983KB）（186）       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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Research on Malicious Behavior Detection and Identification Model  Based on Deep Learning Journal of Information Security Reserach    2023, 9 (12): 1152-.   Abstract （229）      PDF （1897KB）（186）       Knowledge map    Save In order to identify and prevent abnormal behavior and malicious intrusion in networks, a detection model based on Convolutional Neural Network (CNN) and Bidirectional Long ShortTerm Memory (BiLSTM) networks was constructed and applied to various types of Intrusion Detection Systems (IDS). Distinguished from traditional detection models, which suffer from reduced performance due to data redundancy, this model initially feeds the features into a CNN to generate feature mappings, effectively reducing the parameters of the recognition network and automatically eliminating redundant and sparse features. Subsequently, the processed features are used as inputs to the BiLSTM network to detect and recognize malicious behavior within the network. Finally, test results on the NSLKDD and KDD CUP99 datasets demonstrate that the proposed model surpasses existing models in terms of both time efficiency and accuracy, confirming its effectiveness in detecting malicious behavior and accurately classifying network anomalies. Reference | Related Articles | Metrics

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Vulnerability Mining and Threat Detection Journal of Information Security Reserach    2023, 9 (10): 930-.   Abstract （162）      PDF （510KB）（184）       Knowledge map    Save Related Articles | Metrics