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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-.   Abstract （847）      PDF （2555KB）（688）       Knowledge map    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. Reference | Related Articles | Metrics

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Research on the Application of Commercial Cryptography in 5G Network Journal of Information Security Reserach    2023, 9 (4): 331-.   Abstract （626）      PDF （1197KB）（357）       Knowledge map    Save As a new generation of mobile communication network infrastructure, 5G application scenarios run through all aspects of production and life, such as industrial Internet, energy industry, transportation, medical industry and education. However, unprecedented security risks have been brought to 5G networks, including massive terminal access, largescale network deployment, and massive data aggregation. 5G security has gradually become a worldwide research trend in recent years since it is crucial to social development, economic operation, and even national security. Cryptography is the core technology and basic support to assure network and information security. After more than ten years of development, national commercial cryptographic algorithms ZUC, SM4, SM3, SM2, whose independent intellectual property rights are available, have gradually exerted more indispensable effects in maintaining the security of national cyberspace. Starting from the 5G network architecture and interfaces, this paper analyzes the underlying security risks faced by the 5G networks and proposes a corresponding solution as an example in terms of the commercial cryptography application practices of the 5G network. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2023, 9 (3): 206-.   Abstract （539）      PDF （513KB）（350）       Knowledge map    Save Related Articles | Metrics

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Journal of Information Security Reserach    2023, 9 (E1): 105-.   Abstract （535）      PDF （1450KB）（288）       Knowledge map    Save Reference | Related Articles | Metrics

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Towards a Privacy-preserving Research for AI and Blockchain Integration Journal of Information Security Reserach    2023, 9 (6): 557-.   Abstract （534）      PDF （1307KB）（301）       Knowledge map    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. Reference | Related Articles | Metrics

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Application of Penetration Testing for Industrial Control System Terminals Journal of Information Security Reserach    2023, 9 (4): 313-.   Abstract （514）      PDF （3070KB）（191）       Knowledge map    Save The security of industrial control system terminals is getting crucial with the development of the industrial Internet. How to conduct effective safety tests for industrial control system terminals has become a key problem to be studied and solved urgently. In this paper, the general process of penetration testing is firstly introduced, then the application of penetration testing for industrial control system terminals is examined using improper input validation vulnerability as an example. The method starts from information collection and penetration tools to deeply understand the system input verification. Then, during the stage of the vulnerability discovery, the modeling of the vulnerability to sensitive input is proposed, as well as the seed mutation pattern for the industrial control programs is designed. The experiment demonstrates the effectiveness of the proposed method and the vulnerability widely existed in the industrial control systems. This method also discovers the security threats such as data tampering, denial of service, permission access and malicious script injection caused by the input validation vulnerability. At last, this work provides security suggestions for industrial control network security protection and equipment protection. Reference | Related Articles | Metrics

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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-.   Abstract （437）      PDF （1924KB）（299）       Knowledge map    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. Reference | Related Articles | Metrics

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A Survey of SQL Injection Attack Detection and Defense Technology Journal of Information Security Reserach    2023, 9 (5): 412-.   Abstract （413）      PDF （2612KB）（322）       Knowledge map    Save In the era of “Internet+”, data is the most valuable resource of the Internet. Attackers often use SQL injection attacks to destroy the database in order to obtain important data information in the database. The threat to database security is becoming more and more serious. At present, the research on SQL injection attacks mostly focuses on traditional SQL injection attacks, but lacks the cognition of new advanced SQL injection technology with stronger concealment and higher risk, and the research on related detection and defense technology. In response to this phenomenon, this paper analyzes and evaluates traditional and advanced SQL injection attack technologies and their technical characteristics based on the classification of SQL injection technologies; summarizes existing detection and defense technologies, and evaluates the advantages and disadvantages of these methods for defense effectiveness; finally The problems existing in the current research field are sorted out, and suggestions for future research directions are put forward. Reference | Related Articles | Metrics

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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-.   Abstract （406）      PDF （1101KB）（380）       Knowledge map    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. 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 （332）      PDF （1814KB）（239）       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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Research on the Progress of Crossborder Data Flow Governance Journal of Information Security Reserach    2023, 9 (7): 624-.   Abstract （331）      PDF （1036KB）（155）       Knowledge map    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. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2023, 9 (6): 498-.   Abstract （326）      PDF （472KB）（404）       Knowledge map    Save Related Articles | Metrics

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Research on the Application of Commercial Cryptography to Cloud Computing Journal of Information Security Reserach    2023, 9 (4): 375-.   Abstract （320）      PDF （3447KB）（279）       Knowledge map    Save Cloud computing, as a new information processing method, enables users to access information and communication resource services through the network, and it has become an inevitable trend in the development of information technology industry. Users, data, and information resources are highly concentrated, highly dependent on the continuity of cloud platform services, and the scalability of virtualized resources bring inevitable security risks to cloud computing., and the scalability of virtualized resources bring inevitable security risks to cloud computing. Therefore, how to eliminate the security risks of cloud computing by using commercial cryptography technology has become the current research hotspot. This paper starts from the cloud computing network architecture, anlyzes the cryptography application requirements of cloud computing. The paper proposes the corresponding commercial cryptography application scheme for cloud computing scenarios on this basis. The research results provide a theoretical guidance and reference for the application practice of commercial cryptography in cloud computing scenarios, and are expected to solve the key problems of cloud computing security. Reference | Related Articles | Metrics

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A Novel Blockchain Privacy Preserving Scheme Based on Paillier  and FO Commitment Journal of Information Security Reserach    2023, 9 (4): 306-.   Abstract （301）      PDF （934KB）（207）       Knowledge map    Save The blockchain is a shared database with excellent characteristics such as high decentralization and traceability. However, data leakage is still a big problem for blockchain transactions. To order to solve the problem, this paper introduces Paillier homomorphic encryption with variable k (KPH), a privacy protection strategy that hides transaction information by the public key encryption algorithm RSA, performs zeroknowledge proof on the legitimacy of the transaction amount with FO commitment, and updates the transaction amount using the enhanced Paillier semihomomorphic encryption algorithm and verifies the transaction using the FO commitment. Unlike the typical Paillier algorithm, the KPH scheme’s Paillier algorithm includes the variable k and combines the L function and the Chinese remainder theorem to reduce the time complexity from O(|n|2+e) to O(logn), making the algorithm decryption process more efficient. Reference | Related Articles | Metrics

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A Survey of Data Security Sharing Technology Development and  Its Application in Power Domain Journal of Information Security Reserach    2023, 9 (3): 208-.   Abstract （298）      PDF （2019KB）（212）       Knowledge map    Save The circulation, sharing and collaborative application of data elements are the core elements of data element market cultivation in the digital era, and data security sharing technology can effectively realize the secure sharing of data and avoid the phenomenon of “data silos” and privacy leakage. This paper presents a comprehensive review of the latest research achievements and progress of data security sharing technologies in this field. First of all, we outline the development and evolution of data security sharing technologies, and then compare and analyze existing data security sharing solutions in terms of technical features, problem solving, advantages and disadvantages, and summarize the key technologies they rely on and the risks and challenges they face. Secondly, we discuss the application of data security sharing technologies in typical scenarios in the energy and power fields, such as power energy trading, power internet of things, and electric vehicles, providing new ideas and insights for data compliance and governance in the energy and power fields. Finally, the future research directions and development prospects of data security sharing technology applications in the energy and power domain are foreseen. 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 （283）      PDF （806KB）（180）       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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Safety Management of Electronic Display Screen in  Public Areas Journal of Information Security Reserach    2023, 9 (4): 397-.   Abstract （279）      PDF （629KB）（143）       Knowledge map    Save Reference | Related Articles | Metrics

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ChatGPT’s Security Threaten Research Journal of Information Security Reserach    2023, 9 (6): 533-.   Abstract （278）      PDF （1801KB）（250）       Knowledge map    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. Reference | Related Articles | Metrics

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Research on Loop Security Problem in Binary Programs Journal of Information Security Reserach    2023, 9 (4): 364-.   Abstract （274）      PDF （2829KB）（99）       Knowledge map    Save Loop is a common structure in programs and improperly using loop is one of the most important reasons resulting in security problems, making detecting loop security problem is important and valuable. As the path state explosion and loop modeling problems in binary code, statically analyzing of loop security is extremely challenging, and traditional methods are unable to solve these problem. In this paper, we proposed a detecting method for loop security problems based on binary static analyzing，having the ability of detecting out of bound memory access in loop and infinite loop problem. Firstly, we present an accurate extracting and recovering method of loop factors in binary based on analyzing of loop structure and then multiple path explore strategies are utilized to solving the path state explosion and sorting problem. Moreover, we propose a function summary method based on static concrete execution to solving constraints growing problem caused by induction function invoking in loops. Finally, we proposed an inductive analysis method based on loop predicates to detect insecure loop in binary. We have applied our methods on ten real world programs and compared with Angr. The experimental results turn out that our method is capable of detecting more loop problems than Angr. Related Articles | Metrics

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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-.   Abstract （257）      PDF （1143KB）（236）       Knowledge map    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. Reference | Related Articles | Metrics

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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-.   Abstract （247）      PDF （1882KB）（247）       Knowledge map    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. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 236-.   Abstract （244）      PDF （796KB）（219）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2023, 9 (E2): 118-.   Abstract （243）      PDF （1252KB）（96）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Artificial Intelligence Data Falsification Risk  Based on GPT Model Journal of Information Security Reserach    2023, 9 (6): 518-.   Abstract （239）      PDF （1887KB）（209）       Knowledge map    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. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E1): 246-.   Abstract （236）      PDF （1562KB）（152）       Knowledge map    Save Reference | Related Articles | Metrics

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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-.   Abstract （231）      PDF （2305KB）（204）       Knowledge map    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. Reference | Related Articles | Metrics

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Challenges and Responses to Data Governance in China Journal of Information Security Reserach    2023, 9 (7): 612-.   Abstract （229）      PDF （924KB）（199）       Knowledge map    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. 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 （227）      PDF （1252KB）（254）       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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On the Right to Erasure of Personal Information: Characteristics,  Dilemmas and Improvement Paths —From the Perspective of COVID19 Prevention and Control Journal of Information Security Reserach    2023, 9 (4): 356-.   Abstract （226）      PDF （1078KB）（115）       Knowledge map    Save The right to erasure of personal information is an important right enjoyed by individuals in the process of processing personal information under the Personal Information Protection Law. The right to erasure of personal information is both public and private law attributes. From the nature of the public health emergency of the COVID19 and the relevant normative documents issued by China during the epidemic prevention and control period, it can be seen that the current social state can be equivalent to a state of emergency in essence. In this social context, identifying the right to erasure of personal information as a right in public law can effectively play the role of defense and objective law, and then protect the rights and interests of personal information through the protection obligation of public power. Under such logical path, through the establishment of the model of “requested by individualnoticed by power”, the use of the government information disclosure system to disclose personal information processing standards to improve the system design of the right to erasure of personal information, to solve the problems of high cost of exercising rights by COVID19 infected persons and difficulty in performing the obligations of personal information processors in practice. 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 （226）      PDF （1752KB）（154）       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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Survey of Intelligent Vulnerability Mining and Cyberspace Threat Detection Journal of Information Security Reserach    2023, 9 (10): 932-.   Abstract （222）      PDF （1093KB）（205）       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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A Review of Algorithmic Risk and Its Governance in China#br# #br# Journal of Information Security Reserach    2024, 10 (2): 114-.   Abstract （222）      PDF （1781KB）（130）       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 Adversarial Examples Generation Technology Based on  Text Keywords Journal of Information Security Reserach    2023, 9 (4): 338-.   Abstract （216）      PDF （2165KB）（130）       Knowledge map    Save Deep learning models have been widely used to deal with natural language tasks, but the latest research shows that adversarial attacks will seriously reduce the accuracy of the classification model and make the model classification function ineffective. Aiming at the vulnerability of deep learning models when dealing with natural language tasks, a new adversarial examples generation method, KeywordsAttack, is proposed. The method uses a statistical algorithm to select some words to form a text keyword set. And then it iteratively replaces the keywords according to the contribution of the model classification results until the classification model is successfully misled or the number of replacements reaches the set value. According to the characteristics of Chinese, this method generates adversarial examples by splitting Chinese characters and replacing pinyin. Finally, using the public hotel shopping review dataset to conduct experiments, the results show that the average modification magnitude of adversarial examples accounts for 18.2% of the original text and the classification accuracy of attacking the BERT model is reduced by about 43%, and the classification accuracy of attacking the LSTM model is reduced by about 30%. These data show that the KeywordsAttack method can successfully mislead the classification model by making small perturbations to the text. At the same time, the number of query models in the process of generating adversarial examples is small. Reference | Related Articles | Metrics

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A Mechanism Design for Compliance and Trusted Circulation of Data Journal of Information Security Reserach    2023, 9 (7): 618-.   Abstract （215）      PDF （957KB）（135）       Knowledge map    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. Reference | Related Articles | Metrics

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Research and Practice on Data Security Compliance Check  Technology for Operators Journal of Information Security Reserach    2023, 9 (7): 643-.   Abstract （215）      PDF （889KB）（154）       Knowledge map    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. Reference | Related Articles | Metrics

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Comparison Research on Intrusion Detection Model Based on  Machine Learning Journal of Information Security Reserach    2023, 9 (8): 739-.   Abstract （213）      PDF （942KB）（130）       Knowledge map    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. Reference | Related Articles | Metrics

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Research for Zero Trust Security Model Journal of Information Security Reserach    2024, 10 (10): 886-.   Abstract （213）      PDF （2270KB）（211）       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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Local Protection of Power Data Prediction Model Based on Federated Learning and Homomorphic Encryption Journal of Information Security Reserach    2023, 9 (3): 228-.   Abstract （212）      PDF （2070KB）（114）       Knowledge map    Save The accurate and rapid prediction of power data is not only crucial to the stability and  regular operation of the power system but also has a significant impact on the production and life of the entire society. Therefore, efficient and accurate prediction of power data is an essential work in power data research. Recurrent neural networks have excellent performance in power data prediction problems, but require a large amount of data to train the model. Due to privacy concerns, power companies are reluctant to share their electricity data, making it impossible to train more accurate models. In addition, colossal network resource overhead is incurred while uploading massive amounts of data to a central server to train the federated model. To address these problems, this paper combines federal learning with paillier homomorphic encryption algorithm and proposes a local protection method for power data prediction models based on federal learning and homomorphic encryption. It implements the protection of power data and local model parameters, and cotrains the joint model in a secure manner. We conducted experiments using accurate electricity data, and this method achieved good experimental results. Reference | Related Articles | Metrics

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A Review of Adversarial Attack on Autonomous Driving Perception System Journal of Information Security Reserach    2024, 10 (9): 786-.   Abstract （211）      PDF （1560KB）（138）       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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Image Steganalysis Method Based on Multiattention Mechanism and  Siamese Network Journal of Information Security Reserach    2023, 9 (6): 573-.   Abstract （208）      PDF （1439KB）（115）       Knowledge map    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. Reference | Related Articles | Metrics