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A Survey of Anomaly Detection Model for Time Series Data Based on Deep Learning CHEN Hongsong, LIU Xinrui, TAO Zimei, WANG Zhiheng Netinfo Security    2025, 25 (3): 364-391.   DOI: 10.3969/j.issn.1671-1122.2025.03.002 Abstract （1476）   HTML （82）    PDF （34869KB）（250）       Knowledge map    Anomaly detection for time series data is an important area of data mining and network security research. This paper focuses on time series anomaly detection techniques, employing literature survey and comparison analysis to thoroughly examine the applications and research progress of deep learning models in this domain. The research firstly introduced the definition and applications of deep time series anomaly detection, followed by an identification of the nine key challenges faced in this area. Time series anomalies were categorized into ten types, and sixteen typical anomaly detection datasets were enumerated, including five datasets related to social network public opinion security. Deep time series anomaly detection models were classified, the paper categorized and summarized nearly fifty relevant models, including those based on semi-supervised incremental learning for detecting abnormal information disseminators in social networks. Furthermore, the research classified these models into three categories according to their learning modes: reconstruction-based, prediction-based, and a fusion model, their advantages, disadvantages and applications were compared. Finally, the research outlined future research directions for deep time series anomaly detection in eight key areas, providing comprehensive perspects on potential advancements in the fields, potential values and technological bottlenecks were analyzed. Table and Figures | Reference | Related Articles | Metrics

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A Review of Federated Learning Application Technologies HE Zeping, XU Jian, DAI Hua, YANG Geng Netinfo Security    2024, 24 (12): 1831-1844.   DOI: 10.3969/j.issn.1671-1122.2024.12.003 Abstract （1361）   HTML （47）    PDF （17455KB）（227）       Knowledge map    Security problems, such as privacy leakage and reasoning distortion, arising from training and reasoning in AI have heightened concerns, even involving ideology and national strategic security. As an emerging machine learning architecture, federated learning provides effective privacy protection capabilities for multi-party data analysis, processing, and sharing by achieving global model training while maintaining private data locality. Then, from the perspective of research motivation, technical methods, and other aspects of federated learning, how to apply this technology in typical application scenarios to solve practical problems effectively is its core. Therefore, this article conducted a comprehensive survey on the current research status of application technology of federated learning in typical scenarios, which would be valuable to further research and practice of federated learning. Firstly, a comprehensive classification and sorting of relevant literature were conducted from the perspective of research application scenarios, and the research status in each scenario was analyzed from a multidisciplinary perspective. Secondly, from the perspective of technical implementation, a comparative analysis was conducted on the data sets, performance characteristics, evaluation indicators, and other aspects of different schemes in various application scenarios. Finally, the key challenges and development directions faced by federated learning research, especially system applications, were analyzed and summarized. Table and Figures | Reference | Related Articles | Metrics

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Overview of Anomaly Analysis and Detection Methods for Network Traffic LI Hailong, CUI Zhian, SHEN Xieyang Netinfo Security    2025, 25 (2): 194-214.   DOI: 10.3969/j.issn.1671-1122.2025.02.002 Abstract （1267）   HTML （145）    PDF （26268KB）（353）       Knowledge map    With the popularization of the Internet and the increasing threat to network security, the analysis and detection of abnormal characteristics of network traffic have become an important research topic in the field of network security. The article mainly studied the methods of abnormal analysis and detection of network traffic characteristics in recent years. Firstly, the basic concepts and types of network traffic abnormality analysis were introduced. Secondly, the current main anomaly detection technologies were discussed in details, including methods based on statistics, information theory, graph theory, machine learning, and deep learning. Then, common network traffic anomaly detection methods were compared. Finally, the challenges of current research and future development directions were discussed. Table and Figures | Reference | Related Articles | Metrics

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Research on Federated Learning Adaptive Differential Privacy Method Based on Heterogeneous Data XU Ruzhi, TONG Yumeng, DAI Lipeng Netinfo Security    2025, 25 (1): 63-77.   DOI: 10.3969/j.issn.1671-1122.2025.01.006 Abstract （1179）   HTML （32）    PDF （19148KB）（134）       Knowledge map    In federated learning, the need for a large amount of parameter exchange may lead to security threats from untrusted participating devices. In order to protect training data and model parameters, effective privacy protection measures must be taken. Given the imbalanced nature of heterogeneous data, this paper proposed an adaptive differential privacy method to protect the security of federated learning based on heterogeneous data. Firstly, different initial privacy budgets were set for different clients, and Gaussian noise was added to the gradient parameters of the local model; Secondly, during the training process, the privacy budget of each client was dynamically adjusted based on the loss function value of each iteration to accelerate convergence speed; Then, set a trusted central node to randomly exchange the parameters of each layer of local models from different clients, and then uploaded the confused local model parameters to the central server for aggregation; Finally, the central server aggregated the obfuscation parameters uploaded by trusted central nodes, added appropriate noise to the global model based on a pre-set global privacy budget threshold, and performed privacy correction to achieve server level privacy protection. The experimental results show that under the same heterogeneous data conditions, compared to ordinary differential privacy methods, the adaptive differential privacy method proposed in this paper has faster convergence speed and better model performance. Table and Figures | Reference | Related Articles | Metrics

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A Survey of Large Language Models in the Domain of Cybersecurity ZHANG Changlin, TONG Xin, TONG Hui, YANG Ying Netinfo Security    2024, 24 (5): 778-793.   DOI: 10.3969/j.issn.1671-1122.2024.05.011 Abstract （1173）   HTML （296）    PDF （20073KB）（570）       Knowledge map    In recent years, with the rapid advancement of large language model technology, its application potential in various fields such as healthcare and law has become evident, simultaneously pointing to new directions for progress in the field of cybersecurity. This paper began by providing an overview of the foundational theories behind the design principles, training mechanisms, and core characteristics of large language models, offering the necessary background knowledge to readers. It then delved into the role of large language models in enhancing the capabilities to identify and respond to the growing threats online, detailing research progress in areas such as penetration testing, code security audit, social engineering attacks, and the assessment of professional cybersecurity knowledge. Finally, it analyzed the challenges related to security, cost, and interpretability of this technology, and looked forward to the future development direction. Table and Figures | Reference | Related Articles | Metrics

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Hierarchical Clustering Federated Learning Framework for Personalized Privacy-Preserving GUO Qian, ZHAO Jin, GUO Yi Netinfo Security    2024, 24 (8): 1196-1209.   DOI: 10.3969/j.issn.1671-1122.2024.08.006 Abstract （889）   HTML （142）    PDF （14114KB）（114）       Knowledge map    Federated learning (FL) is an emerging framework of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a research hotspot FL concern. Currently, most existing works rely on an independently identical distribution situation and identify malicious gradients using plaintext, which cannot handle the data heterogeneity scenario challenges and imposes significant privacy leakage risks due to releasing unencrypted gradients. To address these challenges, this paper proposed a hierarchical clustering federated learning framework for personalized privacy-preserving. The framework exploited homomorphic encryption by employing the median coordinate as the benchmark. Subsequently, it employed a secure cosine similarity scheme to identify poisonous gradients, and it innovatively utilized clustering as part of the defense mechanism and developed a hierarchical aggregation that enhances the proposed mode’s robustness in IID and non-IID scenarios. Experimental results on the MNIST, CIFAR-10 and Fashion-MNIST datasets indicates that it has powerful privacy-preserving capabilities, and compared to existing defense strategies of FedAVG, PPeFL Media, Trimmed Mean and Clustering, the proposed method achieves an average improvement of 14.90%, 9.59%, 29.50%, 26.57% and 23.19% on accuracy, respectively. Table and Figures | Reference | Related Articles | Metrics

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Security Analysis of Cryptographic Application Code Generated by Large Language Model GUO Xiangxin, LIN Jingqiang, JIA Shijie, LI Guangzheng Netinfo Security    2024, 24 (6): 917-925.   DOI: 10.3969/j.issn.1671-1122.2024.06.009 Abstract （880）   HTML （68）    PDF （19521KB）（285）       Knowledge map    With the extensive application of large language model(LLM) in software development, the role in enhancing development efficiency has also introduced new security risks, particularly in the field of cryptography applications that demand high security. This paper proposed an open-source prompt dataset named LLMCryptoSE, containing 460 natural language description prompts of cryptographic scenarios. It aimed to assess the security of code generated by LLM for cryptographic applications. At the same time, through an in-depth analysis of code snippets generated by LLM, this paper primarily evaluated the misuse of cryptographic API, employing the methodology that combined the static analysis tool CryptoGuard with manual review to conduct a detailed evlatuation of 1380 code snippets. The assessment of three mainstream LLM, including ChatGPT 3.5, ERNIE 3.5, and Spark 3.5, revealed that 52.90% of the code snippets contained at least one instance of cryptographic misuse, with Spark 3.5 showing a relatively better performance with a misuse rate of 48.48%. Based on these findings, the study not only reveals the current challenges in cryptographic application security faced by LLM, but also offers a series of recommendations for LLM users and developers to enhance security. These are aims at providing practical guidance for improving the application of LLM in cryptographic fields. Table and Figures | Reference | Related Articles | Metrics

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A Data-Free Personalized Federated Learning Algorithm Based on Knowledge Distillation CHEN Jing, ZHANG Jian Netinfo Security    2024, 24 (10): 1562-1569.   DOI: 10.3969/j.issn.1671-1122.2024.10.010 Abstract （820）   HTML （681）    PDF （8704KB）（112）       Knowledge map    Federated learning algorithms usually face the problem of huge differences between clients, and these heterogeneities degrade the global model performance, which are mitigated by knowledge distillation approaches. In order to further liberate public data and improve the model performance, DFP-KD trained a robust federated learning global model using datad-free knowledge distillation methods; used ReACGAN as the generator part; and adopted a step-by-step EMA fast updating strategy, which speeded up the update rate of the global model while avoiding catastrophic forgetting. Comparison experiments, ablation experiments, and parameter value influence experiments show that DFP-KD is more advantageous than the classical data-free knowledge distillation algorithms in terms of accuracy, stability, and update rate. Table and Figures | Reference | Related Articles | Metrics

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Research on Centralized Differential Privacy Algorithm for Federated Learning XU Ruzhi, DAI Lipeng, XIA Diya, YANG Xin Netinfo Security    2024, 24 (1): 69-79.   DOI: 10.3969/j.issn.1671-1122.2024.01.007 Abstract （786）   HTML （159）    PDF （13468KB）（249）       Knowledge map    Federated learning has received increasing attention in recent years for breaking down “data silos” with unique training methods. However, when the global model is trained, federation learning is vulnerable to inference attacks, which may reveal the information of some training members and bring about serious security risks. In order to solve differential attacks caused by semi-honest/malicious clients in federated training, this paper proposed a centralized differential privacy federated learning algorithm DP-FedAC. Firstly, the federal accelerated stochastic gradient descent algorithm was optimized to improve the aggregation mode of the server. After calculating the parameter update difference, the global model was updated by gradient aggregation mode to improve the stable convergence. Then, by adding centralized differential Gaussian noise to the aggregation parameters to hide the contributions of training members, the purpose of protecting the privacy information of participants was achieved. Time accounting (MA) was also introduced to calculate privacy loss to further balance the relationship between model convergence and privacy loss. Finally, comparative experiments were conducted with FedAC, distributed MB-SGD, distributed MB-AC-SGD and other algorithms to evaluate the comprehensive performance of DP-FedAC. The experimental results show that the linear acceleration of DP-FedAC algorithm is closest to that of FedAC in the case of infrequent communication, which is far better than the other two algorithms and has good robustness. In addition, the DP-FedAC algorithm achieves the same model accuracy as the FedAC algorithm on the premise of privacy protection, which reflects the superiority and usability of the algorithm. Table and Figures | Reference | Related Articles | Metrics

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New Research Progress on Intrusion Detection Techniques for the Internet of Things FENG Guangsheng, JIANG Shunpeng, HU Xianlang, MA Mingyu Netinfo Security    2024, 24 (2): 167-178.   DOI: 10.3969/j.issn.1671-1122.2024.02.001 Abstract （766）   HTML （316）    PDF （15179KB）（631）       Knowledge map    Compared to traditional intrusion detection mechanisms, the intelligent intrusion detection technology can fully extract data features, demonstrating higher detection efficiency, however, it also imposes greater demands on data sample labels. Considering data sample labels, this article provided a comprehensive review of the latest developments in the intrusion detection technology for the Internet of things(IoT) from the perspectives of supervised and unsupervised learning. Firstly, it outlined signature-based intrusion detection methods and analyzed recent traditional machine learning based intrusion detection methods based on the classification of supervised and unsupervised learning. Then, it analyzed recent deep learning based intrusion detection methods based on supervised, unsupervised, generative adversarial network, and deep reinforcement learning, respectively. Finally, it summarized the research challenges and future trends in the IoT intrusion detection technology. Table and Figures | Reference | Related Articles | Metrics

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Research on the Construction of Zero-Correlation Linear Discriminator for CLEFIA Dynamic Cipher Structure SHEN Xiamin, XIONG Tao, LI Hua, SHEN Xuan Netinfo Security    2024, 24 (6): 948-958.   DOI: 10.3969/j.issn.1671-1122.2024.06.012 Abstract （748）   HTML （13）    PDF （10909KB）（81）       Knowledge map    With the deepening of the research on block cipher application, the design of “dynamic variable” block cipher can effectively improve the application flexibility and deployment security of block cipher algorithm. CLEFIA algorithm follows the idea of “dynamic variable”, some scholars have improved the linear transformation layer of CLEFIA algorithm, so that the diffusion layer in the 6t(t≥1) round can be arbitrarily selected from the {0,1}4 multiple linear bijection transforms. In order to analyze and evaluate the security performance of CLEFIA dynamic cipher structure, this paper mainly adopted the theory of zero-correlation linear analysis, and used the miss-in-the-middle technique and matrix representation method to analyze the zero-correlation linear discriminator of CLEFIA dynamic cipher structure. The results show that under the condition that the wheel function is bijective, no matter what the control parameters μi∈F2,(0≤i≤4) of the dynamic linear layer of CLEFIA dynamic cipher structure are, there are always 8 rounds of zero-correlation linear discriminators. When controlling parameters μ0=0, there are 9 rounds of zero-correlation linear discriminators. Table and Figures | Reference | Related Articles | Metrics

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Review of Encrypted Network Traffic Anonymity and Systemic Defense Tactics WANG Qiang, LIU Yizhi, LI Tao, HE Xiaochuan Netinfo Security    2024, 24 (10): 1484-1492.   DOI: 10.3969/j.issn.1671-1122.2024.10.002 Abstract （726）   HTML （1298）    PDF （12152KB）（762）       Knowledge map    Advanced persistent threat (APT) attacks with complex organization, efficient planning and clear directivity are one of the main threats facing our country, and the trend of covert action and regular attack of APT organizations is becoming more and more obvious. In recent years, it has become more and more difficult for our country to master the main APT activities, which is not unrelated to the fact that APT organizations disappear their attacks into normal information services and network activities, and hide their attack traffic in normal communication traffic. The state in which this kind of highly concealed attack behavior is concealed is called dense state. How to detect dense state behavior and implement system confrontation is one of the bottleneck problems to be solved in the current cyber space defense. From the perspective of clarifying the mechanism of traffic transmission hiding technology for advanced attack activities in cyberspace, this paper puts forward a research framework and countermeasure capability evaluation index system of traffic dense disappearing countermeasure based on two dimensions of anonymous communication link construction and traffic characteristic behavior detection, and comprehensively expounds the relevant research progress, research methods and solutions in recent years. In order to explore the new development direction of dense state countermeasure capability in cyberspace. Table and Figures | Reference | Related Articles | Metrics

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Review of Research on Blockchain-Based Federated Learning LAN Haoliang, WANG Qun, XU Jie, XUE Yishi, ZHANG Bo Netinfo Security    2024, 24 (11): 1643-1654.   DOI: 10.3969/j.issn.1671-1122.2024.11.004 Abstract （715）   HTML （48）    PDF （14856KB）（162）       Knowledge map    As an emerging decentralized distributed machine learning paradigm, blockchain based federated learning not only overcomes the shortcomings of traditional federated learning such as data silos, privacy breaches, and security threats, but also faces new challenges in terms of cost, efficiency, and effectiveness brought by blockchain technology. Therefore, this article first elaborated on federated learning and blockchain by combining basic principles, technical classifications, complementary advantages, and unresolved problems. On this basis, a systematic summary and analysis of current research on blockchain based federated learning was conducted around the architecture, performance, privacy, security, incentive mechanisms, consensus mechanisms, and applications involved in the combination of federated learning and blockchain. Finally, starting from the three dimensions of blockchain based federated learning itself, balance, and application, explored its future research trends and the main problems that urgently need to be solved. Table and Figures | Reference | Related Articles | Metrics

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Anomaly Traffic Detection Based on Deep Metric Learning ZHANG Qiang, HE Junjiang, LI Wenshan, LI Tao Netinfo Security    2024, 24 (3): 462-472.   DOI: 10.3969/j.issn.1671-1122.2024.03.011 Abstract （708）   HTML （66）    PDF （13232KB）（306）       Knowledge map    The identification of network anomalous traffic is one of the important tasks of cyber security nowadays. However, traditional traffic classification models are trained based on traffic data, and most of the traffic data are unevenly distributed, leading to fuzzy classification boundaries, which will greatly limits the classification performance of the model. In order to solve the above problems, this paper proposed a deep metric learning based abnormal traffic detection method. Firstly, a new double-proxy mechanism was designed to improve the efficiency of model training by guiding the optimization direction of updateable proxy through the target proxy compared with the traditional deep metric learning algorithm of single proxy for each category, and to enhance the ability of aggregating traffic data of the same category and separating traffic data of different categories to minimize the intra-class distance and maximized the inter-class distance, which in turn maked the classification of data boundaries more clearly, breaking the performance bottleneck of traditional traffic classification models. Secondly, this paper built neural networks based on 1D-CNN and Bi-LSTM, which can efficiently extract traffic features from spatial and temporal perspectives. The experimental results show that the intra-class distance of NSL-KDD traffic data is significantly reduced and the inter-class distance is significantly increased after the model processing. The intra-class distance decreased by 73.5% compared to the original intra-class distance and the inter-class distance increased by 52.7% compared to the original inter-class distance. And the neural network built in this paper is compared to the widely used deep residual network for deep metric learning with shorter training time and better results. Applying the model proposed in this paper to the traffic classification task on the NSL-KDD and CICIDS2017 datasets, the classification effect is also significantly improved compared to the traditional traffic classification algorithms. Table and Figures | Reference | Related Articles | Metrics

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Data Augmentation Method via Large Language Model for Relation Extraction in Cybersecurity LI Jiao, ZHANG Yuqing, WU Yabiao Netinfo Security    2024, 24 (10): 1477-1483.   DOI: 10.3969/j.issn.1671-1122.2024.10.001 Abstract （682）   HTML （1842）    PDF （8545KB）（300）       Knowledge map    Relationship extraction technology can be used for threat intelligence mining and analysis, providing crucial information support for network security defense. However, relationship extraction tasks in cybersecurity face the problem of dataset deficiency. In recent years, large language model has shown its superior text generation ability, providing powerful technical support for data augmentation tasks. In order to compensate for the shortcomings of traditional data augmentation methods in terms of accuracy and diversity, this paper proposed a data augmentation method via large language model for relation extraction in cybersecurity named MGDA. MGDA used large language model to enhance the original data from four granularities of words, phrases, grammar, and semantics in order to ensure accuracy while improving diversity. The experimental results show that the proposed data augmentation method in this paper effectively improves the effectiveness of relationship extraction tasks in cybersecurity and diversity of generated data. Table and Figures | Reference | Related Articles | Metrics

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A Review of Network Anomaly Detection Based on Semi-Supervised Learning ZHANG Hao, XIE Dazhi, HU Yunsheng, YE Junwei Netinfo Security    2024, 24 (4): 491-508.   DOI: 10.3969/j.issn.1671-1122.2024.04.001 Abstract （680）   HTML （73）    PDF （22842KB）（342）       Knowledge map    The acquisition of network traffic data is relatively easy, while marking the traffic data is comparatively challenging. Semi-supervised learning utilizes a small amount of labeled data and a large amount of unlabeled data for training, reducing the demand for labeled data and effectively adapting to anomaly detection in massive network traffic data. This paper conducted an in-depth investigation into the field of semi-supervised network anomaly detection in recent years. Firstly, it introduced some basic concepts and thoroughly analyzes the necessity of using semi-supervised learning strategies in network anomaly detection. Then, from the perspectives of semi-supervised machine learning, semi-supervised deep learning, and the combination of semi-supervised learning with other paradigms, it analyzed and compared the recent literature on semi-supervised network anomaly detection and summarized the findings. Finally, the current status and future prospects of the field of semi-supervised network anomaly detection were analyzed. Table and Figures | Reference | Related Articles | Metrics

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Federated Learning Backdoor Defense Method Based on Trigger Inversion LIN Yihang, ZHOU Pengyuan, WU Zhiqian, LIAO Yong Netinfo Security    2024, 24 (2): 262-271.   DOI: 10.3969/j.issn.1671-1122.2024.02.009 Abstract （673）   HTML （136）    PDF （12018KB）（179）       Knowledge map    As an emerging distributed machine learning paradigm, federated learning realizes distributed collaborative model training among multiple clients without uploading user original data, thereby protecting user privacy. However, since the server cannot inspect the client’s local dataset in federated learning, malicious clients can embed the backdoor into the global model by data poisoning. Traditional federated learning backdoor defense methods are mostly based on the idea of model detection for backdoor defense, but ignore the inherent distributed feature of federated learning. Therefore, this paper proposed a federated learning backdoor defense method based on trigger inversion. The aggregation server and distributed clients collaborated to generate additional data using trigger reverse technology to enhance the robustness of the client’s local model for backdoor defense. Experiments on different datasets, and the results show that the proposed method can mitigate backdoor attacks effectively. Table and Figures | Reference | Related Articles | Metrics

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Adaptive Sampling-Based Machine Unlearning Method HE Ke, WANG Jianhua, YU Dan, CHEN Yongle Netinfo Security    2025, 25 (4): 630-639.   DOI: 10.3969/j.issn.1671-1122.2025.04.011 Abstract （666）   HTML （27）    PDF （11949KB）（39）       Knowledge map    With the rapid development of artificial intelligence technologies, intelligent systems have been widely applied in various fields such as healthcare and industry. However, once a large amount of user data stored in intelligent systems is maliciously attacked, it will pose a serious threat to user privacy. To protect user data privacy, many countries have introduced relevant laws and regulations to ensure “the right to be forgotten”. Machine unlearning methods are typically divided into exact unlearning and approximate unlearning, aims to adjust model parameters to remove the influence of specific data from a trained model. Exact unlearning methods use the remaining data to retrain the model to achieve unlearning, but this approach is computationally expensive. Approximate unlearning methods use a smaller number of parameter updates to achieve unlearning, but existing approximate unlearning methods suffer from issues such as poor unlearning performance and long unlearning times. This paper proposed an adaptive sampling-based machine unlearning method, the method first sampled the gradients during the model training process, and then used a small amount of gradient information to complete unlearning. It had wide applicability and could be adapted to various machine forgetting methods. The experimental results show that the “sample first, unlearn later” approach can effectively improve the performance of approximate unlearning, while reducing the time for exact unlearning by about 22.9% and the time for approximate unlearning by about 38.6%. Table and Figures | Reference | Related Articles | Metrics

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Analysis of Physical Layer Security Performance in RSMA Wireless Communication Systems under Eavesdropper Attacks HUANG Haiyan, AI Yuxin, LIANG Linlin, LI Zan Netinfo Security    2024, 24 (2): 252-261.   DOI: 10.3969/j.issn.1671-1122.2024.02.008 Abstract （664）   HTML （14）    PDF （11047KB）（170）       Knowledge map    This paper studied a multiple-input single-output downlink communication system based on the Rate Splitting Multiple Access (RSMA) technique. RSMA provids optimal performance by decoding the information of the intended user and treating the information of the remaining users as noise. In addition, the public information in RSMA is not only useful data for the user, but could also be used to interfere with external eavesdroppers. For practical application scenarios where users were far away from the base station, this paper proposed an RSMA-based relay cooperative transmission scheme in the presence of eavesdroppers. The transmission process was divided into two time slots: in the first time slot, the relay receives, decodes and forwards the signal; in the second time slot, the user received the signal from the relay. Each user first decoded the public message and then decoded its private message by applying successive interference cancellation (SIC). Based on this, closed expressions for the outage probability and eavesdropper intercept probability of the system under Rayleigh fading channels were derived. Finally, the correctness of the theoretical analysis was verified by Monte Carlo simulation. The simulation results show that a reasonable choice of the transmit power and the distance between nodes can effectively reduce the interruption probability of the system as well as better trade-off between the security and reliability of the system. Table and Figures | Reference | Related Articles | Metrics

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Subversion Attacks and Countermeasures of SM9 Encryption OUYANG Mengdi, SUN Qinshuo, LI Fagen Netinfo Security    2024, 24 (6): 831-842.   DOI: 10.3969/j.issn.1671-1122.2024.06.002 Abstract （625）   HTML （47）    PDF （13790KB）（229）       Knowledge map    China’s independently developed identity-based encryption algorithm SM9 has been successfully selected as an ISO/IEC international standard. However, adversary can tamper components of cryptographic algorithms to undermine their security. During the initial design of SM9 encryption algorithm, such subversion attacks were not considered. Whether SM9 encryption algorithm is vulnerable to subversion attacks and how to resist subversion attacks is still an unknown issue. To answer the above question, this paper introduced a subversion attack model for identity-based encryption(IBE) and defined two properties: plaintext recoverability and undetectability. In addition, this paper implemented a subversion attack on SM9 encryption algorithm and found that an adversary could recover a plaintext with only two successive ciphertexts. Moreover, this paper proposed a subversion-resilient SM9 encryption(SR-SM9), and proved SR-SM9 was not only secure under the adaptive chosen identity and ciphertext attack(ID-IND-CCA2) but also was subversion-resilient. Finally, this paper implemented SR-SM9 based on gmalg library and Python language. Compared with SM9, SR-SM9 only adds 0.6% computation cost with no additional communication cost. Table and Figures | Reference | Related Articles | Metrics