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    ChatGPT’s Applications, Status and Trends in the Field of Cyber Security
    Journal of Information Security Reserach    2023, 9 (6): 500-.  
    Abstract528)      PDF (2555KB)(467)       Save
    ChatGPT, as a large language model technology, demonstrates extremely strong language understanding and text generation capabilities. It has not only attracted tremendous attention across various industries but also brought new transformations to the field of cybersecurity. Currently, research on ChatGPT in the cybersecurity field is still in its infancy. To help researchers systematically understand the research status of ChatGPT in cybersecurity, this paper provides the first comprehensive summary of ChatGPT’s applications in the field of cybersecurity and potential accompanying security issues. The article first outlines the development of large language model technologies and briefly introduces the technology and features of ChatGPT. Then, it discusses the enabling effects of ChatGPT in the cybersecurity field from two perspectives: assisting attacks and assisting defense. This includes vulnerability discovery, exploitation and remediation, malicious software detection and identification, phishing email generation and detection, and potential use cases in security operations scenarios. Furthermore, the article delves into the accompanying risks of ChatGPT in the cybersecurity field, including content risks and prompt injection attacks, providing a detailed analysis and discussion of these risks. Finally, the paper looks into the future of ChatGPT in the cybersecurity field from the perspectives of security enablement and accompanying security, pointing out the direction for future research on ChatGPT in the cybersecurity domain.
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    Journal of Information Security Reserach    2023, 9 (E1): 105-.  
    Abstract336)      PDF (1450KB)(166)       Save
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    Research on Content Detection Generated by Large Language Model  and the Mechanism of Bypassing
    Journal of Information Security Reserach    2023, 9 (6): 524-.  
    Abstract280)      PDF (1924KB)(200)       Save
    In recent years, there has been a surge in the development of large language models. AI robots like ChatGPT, although they have a largescale security confrontation mechanism inside, attackers can still elaborate questionandanswer patterns to bypass the mechanism, with their help to automatically produce phishing emails and carry out network attacks. In this case, how to identify the text generated by AI robots has also become a hot issue. In order to carry out LLMgenerated content detection experiment, our team collected a certain number of questionandanswer data samples from an Internet social platform and ChatGPT platform, and proposed a series of detection strategies according to different conditions of AI text availability. It includes text similarity analysis based on online controllable AI samples, text data mining based on statistical differences under offline conditions, adversarial analysis based on the LLM generation method under the condition that AI samples are not available, and AI model analysis based on building a classifier by finetuning the target LLM model itself. We calculated and compared the detection capabilities of the analysis engine in each case. On the other hand, we give some antikill techniques against AI text detection engines based on the characteristics of detection strategies, from the perspective of network attack and defense.
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    Key Technologies and Research Prospects of Privacy Computing
    Journal of Information Security Reserach    2023, 9 (8): 714-.  
    Abstract262)      PDF (1814KB)(158)       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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    Journal of Information Security Reserach    2023, 9 (6): 498-.  
    Abstract253)      PDF (472KB)(294)       Save
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    Research on Network Security Governance and Response of  Largescale AI Model
    Journal of Information Security Reserach    2023, 9 (6): 551-.  
    Abstract244)      PDF (1101KB)(187)       Save
    With the continuous development of artificial intelligence technology, largescale AI model technology has become an important research direction in the field of artificial intelligence. The publication of ChatGPT4.0 and ERNIE Bot has rapidly promoted the development and application of this technology. However, the emergence of largescale AI model technology has also brought new challenges to network security. This paper will start with the definition, characteristics and application of largescale AI model technology, and analyze the network security situation under largescale AI model technology. The network security governance framework of largescale AI model is proposed, and the given steps can provide reference for network security work of largescale AI model.
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    Towards a Privacy-preserving Research for AI and Blockchain Integration
    Journal of Information Security Reserach    2023, 9 (6): 557-.  
    Abstract221)      PDF (1307KB)(158)       Save
    With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting the privacy of individuals, these techniques also guarantee the security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, deidentification, multitier distributed ledgers, and kanonymity methods. Moreover, the paper evaluates five critical aspects of AIblockchainintegration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AIblockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve more comprehensive privacy protection of AI privacy.
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    Research on the Progress of Crossborder Data Flow Governance
    Journal of Information Security Reserach    2023, 9 (7): 624-.  
    Abstract209)      PDF (1036KB)(88)       Save
    While promoting the sharing of global data resources, the crossborder data flow will inevitably threaten data sovereignty and national security. The competition for the right to speak in international data with crossborder data flow governance as the game will become the focus of competition in the international community in the future. This paper introduces the background knowledge and constraints of crossborder data flow, investigates and compares the crossborder data flow governance models of the United States, the European Union, Russia, Japan, and Australia, and analyzes the current policy status and challenges of crossborder data flow governance in our country, on this basis, countermeasures and suggestions are proposed for the governance of crossborder data flow in our country from the perspective of data sovereignty, including promoting the classification supervision of crossborder data flow, innovating and developing crossborder data flow governance models, improving countermeasures against extraterritorial “longarm jurisdiction”, and actively participating in and leading the formulation of international governance rules.
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    ChatGPT’s Security Threaten Research
    Journal of Information Security Reserach    2023, 9 (6): 533-.  
    Abstract200)      PDF (1801KB)(170)       Save
    With the rapid development of deep learning technology and natural language processing technology, the large language model represented by ChatGPT came into being. However, while showing surprising capabilities in many fields, ChatgPT also exposed many security threats, which aroused the concerns of academia and industry. This paper first introduces the development history, working mode, and training methods of ChatGPT and its series models, then summarizes and analyzes various current security problems that ChatGPT may encounter and divides it into two levels: user and model. Then, countermeasures and solutions are proposed according to the characteristics of ChatGPT at each stage. Finally, this paper looks forward to developing a safe and trusted ChatGPT and a large language model.
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    Research on Artificial Intelligence Data Falsification Risk  Based on GPT Model
    Journal of Information Security Reserach    2023, 9 (6): 518-.  
    Abstract168)      PDF (1887KB)(144)       Save
    The rapid development and application of artificial intelligence technology have led to the emergence of AIGC (Artificial Intelligence Generated Context), which has significantly enhanced productivity. ChatGPT, a product that utilizes AIGC, has gained popularity worldwide due to its diverse application scenarios and has spurred rapid commercialization development. This paper takes the artificial intelligence data forgery risk as the research goal, takes the GPT model as the research object, and focuses on the possible causes of data forgery and the realization process by analyzing the security risks that have been exposed or appeared. Based on the offensive and defensive countermeasures of traditional cyberspace security and data security, the paper makes a practical study of data forgery based on model finetuning and speculates some data forgery utilization scenarios after the widespread commercialization of artificial intelligence. Finally, the paper puts forward some suggestions on how to deal with the risk of data forgery and provides directions for avoiding the risk of data forgery before the largescale application of artificial intelligence in the future.
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    Challenges and Responses to Data Governance in China
    Journal of Information Security Reserach    2023, 9 (7): 612-.  
    Abstract168)      PDF (924KB)(134)       Save
    At present, data can hold a substantial value in promoting economic and social development, and possess important strategic significance. Data governance has also been a significant topic and practical direction in the development of China’s digital economy and the construction of Digital China. By analyzing the difficulties in the following aspects of data rights confirmation, data security, data compliance, and data circulation, the institutional dilemmas and practical issues faced by data governance are being clarified. And a comprehensive approach for data governance has also been proposed, including protecting data rights and interests, strengthening compliance guidance, stimulating the vitality of the data market, and promoting technological empowerment. It is expected to advance the process of data governance in China.
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    Research and Practice on Data Security Compliance Check  Technology for Operators
    Journal of Information Security Reserach    2023, 9 (7): 643-.  
    Abstract165)      PDF (889KB)(112)       Save
    In the context of the development of the global digital economy, data has become an important asset for enterprises. China positions data as one of the national basic strategic resources and innovative elements of social production. In recent years, the proliferation of ransomware attacks from hackers has posed a significant risk of data leakage to enterprise data security management. Secondly, unconscious data-sharing operations by employees during the production process are also an important way for enterprise data asset leakage. With the promulgation of the Data Security Law, regulatory agencies have made data security reviews a part of the industry security inspections for operators. Therefore, based on regulatory compliance, research and practice related inspection technologies to help operators enhance their security inspection capabilities, ensure data security, and meet the needs of compliance regulation and business development.
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    Research on the Disclosure and Sharing Policy of Cybersecurity  Vulnerabilities in China and the United States
    Journal of Information Security Reserach    2023, 9 (6): 602-.  
    Abstract158)      PDF (2305KB)(122)       Save
    With the increasing scale and complexity of computer software systems, vulnerability attacks on software and systems become more and more frequent, and attack methods become more and more diverse. Various countries have published vulnerability management regulations to avoid the threat of software and system vulnerabilities to national cyberspace security. Proper disclosure and sharing of security vulnerabilities can help security researchers learn security threats quickly and reduce vulnerability repair costs through sharing and communication, which has become essential to mitigating security risks. This paper introduces the public vulnerability database, focuses on the summary of China and the United States network security vulnerability disclosure and sharing related policies and regulations, and gives the possible problems and countermeasures  in vulnerability disclosure and sharing in China so that security researchers can better understand and learn the security vulnerability disclosure process and sharing related regulations, which ensures that security researchers can study security vulnerabilities in the extent permitted by regulations.
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    Research and Thinking on Data Classification and Grading of Important Information Systems#br#
    Journal of Information Security Reserach    2023, 9 (7): 631-.  
    Abstract148)      PDF (1882KB)(169)       Save
    With the development of information technology and networking, incidents surrounding data security are also increasing. The data as a new production factor, is particularly important to ensure the security of important data. The “Data Security Law of the People’s Republic of China” clearly stipulates that the country should establish a data classification and grading protection system to implement classification and grading protection for data. This paper will study China’s data safety management regulations and policies, analyze the the degree of impact and influening objects of data damage, propose specific data classification and grading methods, and provide security protection and governance measures under data classification and grading management based on the industry characteristics and application scenarios of government data. It will achieve the openness and sharing of the data under safety protection, and provide reference for the classification and classification protection of the data in the future.
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    Research on Privacy Protection Technology in Federated Learning
    Journal of Information Security Reserach    2024, 10 (3): 194-.  
    Abstract146)      PDF (1252KB)(169)       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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    Journal of Information Security Reserach    2023, 9 (7): 610-.  
    Abstract142)      PDF (519KB)(142)       Save
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    Consideration on Some Problems in the Development of GPT4 and Its Regulation Scheme
    Journal of Information Security Reserach    2023, 9 (6): 510-.  
    Abstract136)      PDF (1273KB)(87)       Save
    With the release of the new generation of generative artificial intelligence (AI) foundation model GPT4, the era of AI has arrived. GPT4’s rapid popularity also raises some risk issues. In the aspect of data security, facing with frequent data leakage events, data storage period should be set to ensure the parallel development of data security and technology. In the aspect of intellectual property, GPT4 brings challenges on copyright infringement, subject status and works identification, which should be kept in mind in the future. In the aspect of the core algorithm, GPT4 hides the risk of algorithm discrimination. The algorithm should be continuously optimized to make GPT4 towards the true artificial general intelligence. At present, GPT4 is still in the process of continuous development, so it is still too early to design a detailed regulation scheme. In order to better deal with the risks caused by GPT4, independent innovation in the digital age should be sought, and generative AI should be included in the category of deep synthesis technology through special legislation on AI and combined with existing algorithms governing practice.
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    Comparison Research on Intrusion Detection Model Based on  Machine Learning
    Journal of Information Security Reserach    2023, 9 (8): 739-.  
    Abstract134)      PDF (942KB)(82)       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 on the Integration of Full Lifecycle Data Security Management and Artificial Intelligence Technology#br#
    Journal of Information Security Reserach    2023, 9 (6): 543-.  
    Abstract127)      PDF (1143KB)(129)       Save
    With data becoming a new production factor, China has elevated data security to a national strategic level. With the promotion of a new round of technological revolution and the deepening of digital transformation, the artificial intelligence technology has increasing development potential, and gradually empowers the field of data security management actively. Firstly, the paper introduces the concept and significance of data security lifecycle management, analyzes the security risks faced by data in various stages of the lifecycle, and further discusses the problems and challenges faced by traditional data security management technologies in the context of massive data processing and upgraded attack methods. Then, the paper introduces the potential advantages of artificial intelligence in solving these problems and challenges, and summarizes the current mature data security management technologies based on artificial energy and typical application scenarios. Finally, the paper provides an outlook on the future development trends of artificial intelligence technologies in the field of data security management. This paper aims to provide useful references for researchers and practitioners in the field of data security management, and promote the innovation and application of artificial intelligence in the field of data security management technology.
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    Research and Practice on Product Security Governance
    Journal of Information Security Reserach    2023, 9 (12): 1218-.  
    Abstract126)      PDF (2479KB)(70)       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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    A Method of Active Defense for Intelligent Manufacturing  Device Swarms Based on Remote Attestation
    Journal of Information Security Reserach    2023, 9 (6): 580-.  
    Abstract124)      PDF (1988KB)(69)       Save
    With the development of artificial intelligence technology, intelligent manufacturing has become an inevitable choice for enterprise production. However, a compromised device not only causes issues such as confidentiality leaks and production chain errors, but also serves as a springboard for attackers and thus affects the security of the entire swarm. In this paper, we propose a proactive defense solution for intelligent manufacturing swarms based on remote attestation (SecRA). SecRA generates independent challenges for each device, enabling pointtopoint communication between gateways and devices. By extending the functionality of gateways, SecRA utilizes asynchronous communication to adapt to the existing network structure. In addition, based on the challengequery attestation protocol, communication and computation costs are transferred to resourcerich gateways, greatly reducing the burden of devices. Finally, the efficiency and feasibility of the SecRA are experimentally verified.
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    A Mechanism Design for Compliance and Trusted Circulation of Data
    Journal of Information Security Reserach    2023, 9 (7): 618-.  
    Abstract123)      PDF (957KB)(91)       Save
    The circulation of data factors is critical to the development of the digital economy and highquality development of the economy. A trusted and practical data circulation mechanism should satisfy the incentives of all relevant participants simultaneously. The mechanism should be accompanied by an immediate regulation mechanism in data right authentication, registration, circulation, delivery and settlement to protect national information security and individual privacy exante. The rules of the mechanism should be observable to all so that a trusted consensus is established. The difference in features of data from tangible and intangible assets in physical existence, legal authentication, exclusiveness in use and relevant supporting techniques implies that a trusted data circulation mechanism should combine both theories of law, economics, management science and information techniques in designing circulation form, supplyside incentive, consistency in operation and screening signals in demandside.
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    Image Steganalysis Method Based on Multiattention Mechanism and  Siamese Network
    Journal of Information Security Reserach    2023, 9 (6): 573-.  
    Abstract119)      PDF (1439KB)(59)       Save
    Aiming at the problem of extracting more significant steganographic features from images to improve detection accuracy of steganalysis detection, a Siamese network image steganalysis method based on multiattention mechanism is proposed. This method uses the idea of feature fusion to make the steganalysis model extract richer steganographic features. Firstly, a Siamese network subnetwork composed of ParNet block, depthwise separable convolution block, normalizationbased attention module, squeeze and excitation module, and external attention module is designed, and the multibranch network structure and multiattention mechanism are used to extract more useful classification results. Features improve the detection ability of the model; then use Cyclical Focal loss to modify the weight of the training samples at different stages of training to improve the training effect of the model. The experiment uses the BOOSbase 1.01 data set to conduct experiments on five adaptive steganography algorithms: WOW, SUNIWARD, HUGO, MiPOD and HILL. Experimental results show that this method outperforms SRNet, ZhuNet and SiaStegNet methods in detection accuracy, and has a lower number of parameters.
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    Android Malware Multiclassification Model Based on Transformer
    Journal of Information Security Reserach    2023, 9 (12): 1138-.  
    Abstract118)      PDF (2073KB)(114)       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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    Research on Vulnerability Text Feature Classification Technology  Based on BERT
    Journal of Information Security Reserach    2023, 9 (7): 687-.  
    Abstract114)      PDF (944KB)(95)       Save
    With the development of informatization and the increase of network applications, many software and hardware products are affected by various types of cybersecurity vulnerabilities. Vulnerability analysis and management often require people to classify large amounts of vulnerability intelligence texts. In order to efficiently and accurately determine the category of the vulnerability described by the vulnerability intelligence text, this paper proposes a cybersecurity vulnerability classification model based on BERT (bidirectional encoder representation from Transformers). First, the vulnerability classification dataset is constructed, and the pretrained model represents the vulnerability intelligence text as feature vectors. Then the feature vectors complete the classification through the classifier. At last, we use the test set to evaluate the classification effect. In our experiment, we use TextCNN, TextRNN, TextRNN_Att, fastText and the proposed model to classify 48000 vulnerability intelligence texts containing vulnerability descriptions. Experimental results show that the proposed model scored the highest on the classification evaluation indicators on the test set, and it can be effectively applied to cybersecurity vulnerability classification tasks and reduce manual workload.
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    Research on Image Steganography and Extraction Scheme Based on  Implicit Symmetric Generative Adversarial Network
    Journal of Information Security Reserach    2023, 9 (6): 566-.  
    Abstract110)      PDF (953KB)(70)       Save
    Aiming at the problems in the image steganography technology that the quality of the carrier image is degraded and vulnerable to attacks when the secret image is embedded, this paper proposes an image steganography and extraction scheme based on an implicit symmetric generative network. The scheme first abstracts the task of image steganography and extraction into a mathematical optimization problem. Secondly, an implicit symmetric generative adversarial network model is proposed according to the optimization problem. The implicit symmetric generative adversarial network contains two independent generative adversarial subnetworks, namely the steganographic adversarial subnetwork and the extraction adversarial subnetwork. In the steganographic confrontational subnetwork, first the encoder converts the cover image and the covert image into a set of highdimensional feature vectors containing enough cover image information and secret image information. The decoder then reconstructs these feature vectors into images embedded with secret information. In the extraction adversarial subnetwork, the image embedded with secret information is passed through another set of encoder and decoder to extract the hidden image. Finally, a loss function suitable for the model is designed. Experimental results show that the proposed scheme has high image quality and can maintain good robustness in the face of various common attacks.
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    Research and Application of EndtoEnd Traceability Technology for Government Data
    Journal of Information Security Reserach    2023, 9 (7): 655-.  
    Abstract108)      PDF (1997KB)(92)       Save
    Along with the national digital government strategic layout’s continuous advancement, in order to give full play to the benefits of big data aggregation and analysis, the nodes of digital government deal with a large amount of important data, and the data communication and information sharing among the nodes, and the data security risk is exposed day by day, which brings a great challenge to trace the source of data leakage events. This paper first analyzes the risk scenario of digital government data leakage. Then, based on the domestic and foreign wellknown traceability models 7W, ProVOC and so on, the endtoend traceability model and technical method are proposed. The model is a comprehensive application of the annotation method and the reverse query method. The method is a scenariobased improvement of database watermarking, dynamic desensitization and other technologies, and makes use of big data and association analysis technology, the traceability technology and the landing practice strategy are formed through each link of the service data flow, including data marking, staining, data operation log association analysis. Finally, taking a government core node network environment as an example, the paper carries on the application practice research, and achieves the effect of successfully tracing the evidence chain of data leakage exit and data access transmission chain, it improves the traceability efficiency and accuracy of data security events.
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    Vulnerability Mining and Threat Detection
    Journal of Information Security Reserach    2023, 9 (10): 930-.  
    Abstract107)      PDF (510KB)(121)       Save
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    Security Risks and Countermeasures to Artificial Intelligence#br#
    #br#
    Journal of Information Security Reserach    2024, 10 (2): 101-.  
    Abstract104)      PDF (469KB)(139)       Save
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    A Secure Data Sharing Scheme Supporting Finegrained Authorization
    Journal of Information Security Reserach    2023, 9 (7): 667-.  
    Abstract103)      PDF (1681KB)(93)       Save
    Considering the problems such as centralized data storage and difficulty in data sharing in cloud computing environments, based on the combination of multiconditional proxy reencryption and attributebased proxy reencryption, a multiconditional attributebased threshold proxy reencryption scheme which supports multiple authorization conditions is proposed. The scheme supports finegrained access to ciphertext data under multiple keyword authorization conditions, and can limit the authorization conditions and scope of ciphertext sharing. Only when the attribute set meets the access structure in the ciphertext and the keywords are consistent with the keywords set in the ciphertext, users can access the data. This solution achieves finegrained access to ciphertext data under multiple keyword authorization conditions, supports flexible user revocation, prevents unauthorized decryption of ciphertext by conspirators, and protects the sensitive information of data owners. Through the provable security analysis, it is shown that under the general group model, the scheme can resist chosen plaintext attack; compared with other conditional proxy reencryption schemes, the functions it supports are more diverse.

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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-.  
    Abstract102)      PDF (2613KB)(79)       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-.  
    Abstract102)      PDF (1096KB)(110)       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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    Application Study on Weibo Network Public Opinion Communication  Based on Social Network Analysis
    Journal of Information Security Reserach    2023, 9 (7): 693-.  
    Abstract101)      PDF (1645KB)(64)       Save
    Hot topics of public concerns over social events often capture wide attention. Research on the social network structure of the events helps the guidance on network public opinion in a more effective way. Analyzing three aspects of density interval, centrality and cohesive subgroup that is based on social network analysis (SNA) and Ucinet software, we focus on the hot topics of public concerns over social events in recent five years between 2017 and 2022, and we study in this paper the network public opinion communication of the topics through social media platform Weibo and how it applied research in the network structure of social events. The result presents the network structure of high connectivity between nodes, low interaction and core positions of some Weibo common users nodes and Weibo celebrities nodes in their increasing influence. Therefore, ordinary audience, to a certain extent, are much more likely to get attracted to and involved in network public opinion on hot topics of public concerns over social events. The conclusion of this application study on social network analysis can provide a theoretical reference for the strategies relating to guidance on network public opinion.
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    Neural Network Backdoor Detection Method Based on Multilevel  Measurement Difference
    Journal of Information Security Reserach    2023, 9 (6): 587-.  
    Abstract101)      PDF (950KB)(50)       Save
    The deep neural network has achieved advanced performance in various tasks. However, due to the lack of transparency and unexplainable of the deep learning model, the model will show abnormal behavior when the backdoor set by the malicious attacker is triggered in the reasoning stage, and the performance will be degraded. To solve the above problems, this paper proposes a Backdoor Detection Scheme Based on Multilevel Measurement Difference (MultMeasure). Test cases are generated against the source model and the authorization model maliciously injected backdoor. Two measures, white box and black box, are set to calculate test cases. Finally, the statistical threshold is used to calculate the difference to determine whether the model is injected backdoor. Experiments show that MultMeasure proposed in this paper is tested in the backdoor attack scenario implanted with Trojan Horse model, and performance evaluation is good under multiple triggers and invisible triggers. Compared with the existing detection schemes in recent years, MultMeasure has better effectiveness and stability.
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    The Status and Trends of Confidential Computing
    Journal of Information Security Reserach    2024, 10 (1): 2-.  
    Abstract101)      PDF (1466KB)(133)       Save
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    Detection and Classification Method of Network Attacks Based on  Generalized Neural Networks
    Journal of Information Security Reserach    2023, 9 (6): 593-.  
    Abstract97)      PDF (1705KB)(75)       Save
    Nowadays, the virtual world is becoming more and more complex, and network attacks and emerging security threats are gradually increasing. Therefore, it is necessary to study intelligent detection and classification methods for network attacks to comprehensively observe network activities and prevent malicious behaviors. It is proposed that an intrusion detection system based on generalized regression neural network (GRNN) in this paper, which can intelligently detect and classify malicious network attacks, and be tested by using today’s mainstream NSLKDD dataset. The experimental results show that the technology proposed in this paper can identify and classify malicious behaviors more effectively than other current attack detection technologies.
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    Encrypted Proxy Traffic Identification Method Based on Convolutional Neural Network#br#
    Journal of Information Security Reserach    2023, 9 (8): 722-.  
    Abstract93)      PDF (2382KB)(68)       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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    Research on Network Malicious Traffic Detection Technology Based on  Ensemble Learning Strategy
    Journal of Information Security Reserach    2023, 9 (8): 730-.  
    Abstract92)      PDF (2586KB)(100)       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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    Exploration and Research on Security Guarantee of Data  Transaction and Circulation
    Journal of Information Security Reserach    2023, 9 (7): 662-.  
    Abstract92)      PDF (1035KB)(73)       Save
    tWith the continuous development of Internet technology, data transaction and circulation have become a global trend. However, the security problem of data transaction and circulation is also increasingly prominent. From the perspective of the security guarantee of data transaction and circulation, this paper discusses the security problems of data transaction and circulation, and puts forward a security framework and solutions in order to provide some references for the security guarantee of data transaction and circulation.
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    Survey of Intelligent Vulnerability Mining and Cyberspace Threat Detection
    Journal of Information Security Reserach    2023, 9 (10): 932-.  
    Abstract92)      PDF (1093KB)(95)       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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