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    Journal of Information Security Reserach    2023, 9 (E1): 105-.  
    Abstract376)      PDF (1450KB)(190)       Save
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    Key Technologies and Research Prospects of Privacy Computing
    Journal of Information Security Reserach    2023, 9 (8): 714-.  
    Abstract281)      PDF (1814KB)(191)       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.
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    Research on Privacy Protection Technology in Federated Learning
    Journal of Information Security Reserach    2024, 10 (3): 194-.  
    Abstract172)      PDF (1252KB)(200)       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.
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    Comparison Research on Intrusion Detection Model Based on  Machine Learning
    Journal of Information Security Reserach    2023, 9 (8): 739-.  
    Abstract148)      PDF (942KB)(96)       Save
    Nowadays, network threats are constantly evolving and demonstrate increasing invisibility. Studying the performance and characteristics of multiple machine learning models for intrusion detection on modern traffic data is of greater significance to improve the timeliness of intrusion detection systems. This paper explores the use of recent efficient machine learning models, including ensemble learning(Random Forest, XGBoost, LightGBM) and deep learning(CNN, LSTM, GRU, etc) models for intrusion detection tasks on the public dataset UNSWNB15.We elaborate the task flow and experimental configuration, compare and analyze the experimental results of different models, summarize the characteristics of each model in the network intrusion detection task. The experimental results demonstrate that, under a 10% sampled dataset of UNSWNB15, the bestperforming model for the binary classification task among the experimental models is LightGBM, with an F1 score of 0.897, an accuracy of 89.86%, a training time of 1.98s, and a prediction time of 0.11s. In the case of multiclassification tasks, the most comprehensive prediction model among the experimental models is XGBoost, with an overall F1 score of 0.7907, an accuracy of 75.96%, a training time of 144.79s, and a prediction time of 0.21s.
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    Research and Practice on Product Security Governance
    Journal of Information Security Reserach    2023, 9 (12): 1218-.  
    Abstract133)      PDF (2479KB)(80)       Save
    This paper studies how to ensure that suppliers deliver secure and trustworthy products and services from the perspective of product security governance. First, this paper introduces the context of product security, gives the definition and objectives of product security, and proposes that product security is a security governance problem. Then this paper establishes the organizational structure of product security governance based on the threeline model, describes the roles and responsibilities of each organizational unit, and solves the problems of separation of duties and conflicts of interest from the organizational structure. Next this paper introduces the concept, framework, system and implementation approaches of product security policies, and establishes the toplevel requirements of product security system construction. Finally, the contribution of this paper is summarized and the research direction for the next step is pointed out. These research results have been applied in ZTE’s product security practices and have achieved good governance effects.
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    Android Malware Multiclassification Model Based on Transformer
    Journal of Information Security Reserach    2023, 9 (12): 1138-.  
    Abstract128)      PDF (2073KB)(123)       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.
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    Security Risks and Countermeasures to Artificial Intelligence#br#
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    Journal of Information Security Reserach    2024, 10 (2): 101-.  
    Abstract126)      PDF (469KB)(175)       Save
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    Vulnerability Mining and Threat Detection
    Journal of Information Security Reserach    2023, 9 (10): 930-.  
    Abstract114)      PDF (510KB)(129)       Save
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    The Status and Trends of Confidential Computing
    Journal of Information Security Reserach    2024, 10 (1): 2-.  
    Abstract113)      PDF (1466KB)(147)       Save
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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-.  
    Abstract108)      PDF (2613KB)(91)       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.
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    Security and Privacy Protection in 6G Network: A Survey
    Journal of Information Security Reserach    2023, 9 (9): 822-.  
    Abstract108)      PDF (1096KB)(121)       Save
    The scale of 5G network deployments continues to grow. While there are obvious advantages over 4G network, the limitations of 5G network are emerging, which leads to research on 6G network technologies. The complexity of 6G network and the diversity of 6G’s applications make the security issues of 6G more prominent. Coupled with the fact that 6G frameworks and related technologies are largely in a conceptual state, the security and privacy issues of 6G network are still in the exploratory stage. In this paper we analyzed the current state of 6G security and privacy research at first, and than pointed out the security challenges in 6G network, discussed potential security solutions for 6G network from the aspects of physical layer security, artificial intelligence (AI), distributed ledger technology (DLT), and edge computing, and finally we provided an outlook on future research trends of security and privacy protection in 6G network.
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    Malicious Client Detection and Defense Method for Federated Learning
    Journal of Information Security Reserach    2024, 10 (2): 163-.  
    Abstract105)      PDF (806KB)(102)       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.
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    Classification and Grading Method of Transportation Government Data
    Journal of Information Security Reserach    2023, 9 (8): 808-.  
    Abstract102)      PDF (1008KB)(123)       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.
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    Survey of Intelligent Vulnerability Mining and Cyberspace Threat Detection
    Journal of Information Security Reserach    2023, 9 (10): 932-.  
    Abstract101)      PDF (1093KB)(111)       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.
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    Analysis of Security Blind Area of Large LAN#br#
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    Journal of Information Security Reserach    2024, 10 (4): 335-.  
    Abstract100)      PDF (784KB)(86)       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.
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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-.  
    Abstract99)      PDF (2586KB)(109)       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.
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    A Network Intrusion Detection Model Integrating CNN-BiGRU and  Attention Mechanism
    Journal of Information Security Reserach    2024, 10 (3): 202-.  
    Abstract98)      PDF (2042KB)(129)       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.
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    Malicious TLS Traffic Detection Based on Graph Representation#br#
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    Journal of Information Security Reserach    2024, 10 (3): 209-.  
    Abstract97)      PDF (1728KB)(91)       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.
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    Encrypted Proxy Traffic Identification Method Based on Convolutional Neural Network#br#
    Journal of Information Security Reserach    2023, 9 (8): 722-.  
    Abstract96)      PDF (2382KB)(74)       Save
    A method for identifying encrypted proxy traffic based on convolutional neural network is proposed. First, the stream reassembly operation is performed on the selfdeployed and selfcaptured raw encrypted traffic, and then the first L×L bytes of the first N data packets of the restored data stream are extracted to form a grayscale image as the stream feature image of the data stream whose (Height, Width, Channel) is (N×L, L, 1). After that, all the samples are divided into training set, verification set, and test set, which are utilized by the designed convolutional neural network model for training, verification and testing respectively. Finally, by selecting different combinations of the first N data packets and the packet length strategy L to conduct experiments, it is finally measured that when N=4, L=40×40, the highest identification accuracy of the model can reach 99.38%, which has certain advantages in terms of accuracy compared with other related similar methods.
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    Data Life Cycle Safety Monitoring Method Driven by Big Data
    Journal of Information Security Reserach    2023, 9 (12): 1226-.  
    Abstract94)      PDF (1859KB)(98)       Save
    Aiming at the problems of small coverage, low monitoring accuracy and low automation of traditional data monitoring methods, a data lifecycle safety monitoring method driven by large data is put forward, which is based on feature analysis recognition model, content segmentation model, realtime data monitoring model, file analysis retrieval model and user abnormal behavior prediction model to monitor data security risk in realtime. It effectively guarantees the safe flow of data assets. After testing, the overall accuracy of sensitive data collection, sensitive page capture, sensitive flow monitoring and sensitive file parsing under this method is higher than 92%, and the accuracy of user’s sensitive behavior prediction is higher than 93%, which effectively improves the monitoring range and accuracy of sensitive data.
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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-.  
    Abstract92)      PDF (874KB)(109)       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.
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    Research on Performance of MAVSec Security Protocol Based on  Different Cryptographic Algorithms
    Journal of Information Security Reserach    2023, 9 (8): 771-.  
    Abstract92)      PDF (2065KB)(55)       Save
    As a lightweight communication protocol between the UAV and the ground control center, MAVLink has the advantages of convenient configuration and easy invocation. A twoway channel is established between the UAV and the ground control center through MAVLink to transmit control information and status position data. However, MAVLink does not support encrypted communication and authentication and authorization mechanisms, which has potential risks of being attacked. The MAVSec protocol is an encrypted version of MAVLink proposed by Allouch A et al. In this paper, the performance of Chinese commercial cryptographic algorithms and foreign cryptographic algorithms in terms of encrypted transmission delay, memory usage and CPU consumption for MAVSec protocols using were evaluated different encryption algorithms. The experimental results show that, compared with other encryption algorithms, the ZUC algorithm in China’s commercial cryptographic algorithms has better performance and efficiency when transmitting command data, and occupies less CPU and memory resources. The application of ZUC algorithm in MAVLink improved the security of communication without affecting the performance, and saved the computing resources and battery consumption of the drone to the greatest extent.
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    Research on Security Risks and Protection of Container Images
    Journal of Information Security Reserach    2023, 9 (8): 792-.  
    Abstract92)      PDF (1788KB)(132)       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.
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    Journal of Information Security Reserach    2023, 9 (E2): 13-.  
    Abstract92)      PDF (1022KB)(121)       Save
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    Journal of Information Security Reserach    2023, 9 (E2): 2-.  
    Abstract91)      PDF (361KB)(81)       Save
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    Journal of Information Security Reserach    2023, 9 (E1): 4-.  
    Abstract87)      PDF (1410KB)(86)       Save
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    Face Recognition Privacy Protection Method Based on Homomorphic Encryption#br#
    Journal of Information Security Reserach    2023, 9 (9): 843-.  
    Abstract86)      PDF (1144KB)(86)       Save
    With the development and application of big data, biometric recognition technology has developed rapidly and has been widely used in new authentication technology. Because the traditional biometricbased identity authentication is mostly carried out in plaintext, and the user’s privacy cannot be adequately guaranteed, this paper proposes and designs a face recognition privacy protection method based on homomorphic encryption technology based on the above defects. This method firstly uses the current popular authentication model FaceNet to extract the user’s biometric information, and then encrypts the extracted feature information with the help of RLWE based homomorphic encryption technology to ensure that when the biometric information is outsourced to the server for distance calculation, the user’s private data will not be disclosed and the server will not snoop on the user’s behavior. At the same time, in the process of identity authentication, the concept of random number is introduced to prevent illegal users from replaying attacks on the server.Experiments show that the method can still ensure high accuracy and feasibility in the state of ciphertext.
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    Research on Identity Authentication Technology Based on Block Chain and PKI
    Journal of Information Security Reserach    2024, 10 (2): 148-.  
    Abstract85)      PDF (1573KB)(147)       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.
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    Research on Source Code Vulnerability Detection Based on BERT Model
    Journal of Information Security Reserach    2024, 10 (4): 294-.  
    Abstract84)      PDF (3199KB)(113)       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.
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    Research and Practice of Government Data Security Governance System
    Journal of Information Security Reserach    2023, 9 (9): 900-.  
    Abstract84)      PDF (4365KB)(97)       Save
    Critical data is an important engine that keeps organizations and societies going, which makes it a target for malicious hackers, a target for intense scrutiny by regulators, and a need to prevent employees from inadvertently disclosing secret internal information. As the “guardian” to maintain the order of data opening and ensure data security, government departments should not only protect the security of sensitive data, maintain regulatory compliance, but also quickly deploy and implement data security protection projects without changing the existing business processes, and effectively control costs and reduce complexity and risks. This paper starts from the background of government data security governance, through the analysis of the government data security management status and security management needs, refer to the latest data security governance theory and technology research results at home and abroad, according to the relevant policies, laws and regulations, based on the security needs under the background of information technology application innovation, puts forward the framework of the system which applies to the security governance of government affairs data in our country, introduces the related technologies and project practice case.
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    Journal of Information Security Reserach    2023, 9 (E1): 55-.  
    Abstract84)      PDF (1602KB)(53)       Save
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    Research on Locally Verifiable Aggregate Signature Algorithm Based on SM2
    Journal of Information Security Reserach    2024, 10 (2): 156-.  
    Abstract81)      PDF (983KB)(92)       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.
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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-.  
    Abstract80)      PDF (1897KB)(70)       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.
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    Research on the Security Architecture of Artificial Intelligence  Computing Infrastructure
    Journal of Information Security Reserach    2024, 10 (2): 109-.  
    Abstract77)      PDF (1146KB)(108)       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.
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    Generative Fake Speech Security Issue and Solution#br#
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    Journal of Information Security Reserach    2024, 10 (2): 122-.  
    Abstract77)      PDF (1170KB)(83)       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.
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    Research on Location Attack Detection of VANET Based on Incremental Learning
    Journal of Information Security Reserach    2024, 10 (3): 277-.  
    Abstract75)      PDF (1866KB)(77)       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.
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    Research on the Practice of DevSecOps in the Construction of  Digital Government
    Journal of Information Security Reserach    2023, 9 (12): 1210-.  
    Abstract75)      PDF (1906KB)(82)       Save
    As an important carrier of data, government business systems are often the most important targets of attack, and government security construction pays more attention to compliance requirements, ensuring business operation through security products and services, while application endogenous security is ignored. In order to adapt to the high security requirements of the current digital government and meet the current scenario of intensive digital government construction, it is necessary to shift security to the left and focus on supply chain and application endogenous security. The government’s information project construction model needs to prioritize development work, and security needs to be closely integrated with the research and development process. DevSecOps, as an emerging security development model, has entered the field of digital government application development. The application development security system enabled by DevSecOps can improve the development process, reduce security repair costs, shorten development cycles, and greatly enhance the level of digital government security.

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    Survey of Research on Key Technologies of Internet Content Security
    Journal of Information Security Reserach    2024, 10 (3): 248-.  
    Abstract74)      PDF (1234KB)(85)       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.
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    Journal of Information Security Reserach    2023, 9 (E2): 37-.  
    Abstract74)      PDF (1790KB)(90)       Save
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    Research on Text Classification Model Based on Federated Learning  and Differential Privacy
    Journal of Information Security Reserach    2023, 9 (12): 1145-.  
    Abstract73)      PDF (2101KB)(78)       Save
    As a distributed machine learning framework, federated learning can complete model training without disclosing user data. However, recent attacks have shown that only keeping the locality of data in the training process can not provide sufficient privacy protection. Therefore, in order to address the privacy protection issues during federated learning training, this paper proposes a text classification model based on BERT. This model combines differential privacy (DP) and federated learning (FL) to ensure that the federated model training process is protected from inference attacks during the transfer of federated learning parameters. The final experiment shows that the proposed method can maintain high model accuracy while protecting privacy.
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