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Journal of Information Security Reserach    2024, 10 (E2): 105-.   Abstract （618）      PDF （929KB）（345）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 27-.   Abstract （342）      PDF （763KB）（184）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 59-.   Abstract （306）      PDF （1210KB）（102）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 24-.   Abstract （279）      PDF （555KB）（239）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 68-.   Abstract （279）      PDF （1105KB）（144）       Knowledge map    Save Reference | Related Articles | Metrics

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A Trust Framework for Large Language Model Application Journal of Information Security Reserach    2024, 10 (12): 1153-.   Abstract （272）      PDF （1420KB）（208）       Knowledge map    Save The emergence of large language model has greatly propelled the rapid application of artificial intelligence across various domains. In practice, however, there are a series of security and trust challenges in the applications of large language models caused by “model hallucinations”. These challenges make it difficult for practical applications to trust and adopt the results returned by the large language models, especially in securityrelated application domains. In many professional fields, we find that there lacks a unified technical framework to ensure the trustworthiness of results returned by large language models, which seriously hinders the application of largescale model technology in professional fields. To address this issue, a largescale model trusted application framework DKCF, integrating sufficient data (D), expertise knowledge (K), intellectual collaboration (C), and efficient feedback (F), is proposed. This framework is developed based on our practical applications in professional fields such as finance, healthcare, and security. We believe that DKCF can shed light on secure and reliable applications of large language models, and facilitate the intellectual revolution across various professional domains. Reference | Related Articles | Metrics

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Overview of Regulation of Crossborder Data Flow Journal of Information Security Reserach    2025, 11 (2): 164-.   Abstract （271）      PDF （1274KB）（139）       Knowledge map    Save The development of the digital economy has made crossborder data flow an inevitable trend, and while bringing economic benefits, the security of crossborder data flow cannot be ignored. Due to the complexity of the subjects and scenes involved in the process of crossborder data flow, and the uncontrollability of the process, how to regulate the possible security problems in the process of crossborder data flow has become the focus of the world. So far, there is no unified governance rule system for crossborder data flow in the world, and at the same time, there are huge differences in legislation on crossborder data flow in different countries, which results in the complex situation of legislation on crossborder data flow in the world. This paper describes the current situation of crossborder data flow from the perspectives of laws and regulations, bilateral agreements and standards, and in this way develops horizontal comparisons, sorts out the existing regulatory differences, analyzes the challenges and opportunities China faces under the current trend, and gives reasonable countermeasures. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 88-.   Abstract （251）      PDF （684KB）（104）       Knowledge map    Save Reference | Related Articles | Metrics

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Keytarget Face Recognition Scheme Based on Homomorphic  Encryption and Edge Computing Journal of Information Security Reserach    2024, 10 (11): 1004-.   Abstract （250）      PDF （2205KB）（92）       Knowledge map    Save With the promotion of China’s comprehensive national strength and international status, more and more major international events are held in China’s firsttier cities, such as the 31st Chengdu Universiade and the 19th Hangzhou Asian Games. The huge flow of people and complex crowd categories have caused considerable security pressure on the security team. Because the traditional face recognition system realizes face recognition in the central server in plaintext state and relies on the traditional state secret algorithm to ensure security, the computational efficiency and security of the whole system cannot be fully guaranteed. Therefore, based on the CKKS homomorphic encryption scheme and Insightface face recognition algorithm, this paper proposes a keytarget face recognition scheme supporting edge computing. Firstly, the key face features are encrypted by the CKKS homomorphic encryption scheme, and the ciphertext data are distributed to each frontend monitoring device. After that, the frontend monitoring device is responsible for extracting the face features of the scene crowd and calculating the matching degree with the ciphertext database. Finally, the ciphertext calculation results are returned to the central server and decrypted. Experimental results show that the recognition accuracy of the proposed scheme is 98.2116% when the threshold is 1.23 on LFW data sets, which proves the reliability of the proposed scheme. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 117-.   Abstract （250）      PDF （625KB）（128）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 7-.   Abstract （248）      PDF （1507KB）（112）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 40-.   Abstract （247）      PDF （839KB）（110）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 32-.   Abstract （244）      PDF （3674KB）（185）       Knowledge map    Save Reference | Related Articles | Metrics

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Design of Adversarial Attack Scheme Based on YOLOv8 Object Detector Journal of Information Security Reserach    2025, 11 (3): 221-.   Abstract （239）      PDF （3519KB）（65）       Knowledge map    Save Currently, cameras equipped with AI object detection technology are widely used. However, AI object detection models in realworld applications are vulnerable to adversarial attacks. Existing adversarial attack methods, primarily designed for earlier models, are ineffective against the latest YOLOv8 object detector. To address this issue, we propose a novel adversarial patch attack method specifically for the YOLOv8 object detector. This method minimizes confidence output while incorporating an exponential moving average (EMA) attention mechanism to enhance feature extraction during patch generation, thereby improving the attack’s effectiveness. Experimental results demonstrate that our method achieves superior attack performance and transferability. Validation tests, in which the adversarial patches were printed on clothing, also demonstrated excellent attack results, indicating the strong practicality of our proposed method. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 2-.   Abstract （235）      PDF （1381KB）（151）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 266-.   Abstract （226）      PDF （1927KB）（115）       Knowledge map    Save Reference | Related Articles | Metrics

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An Optimized Computation Method for Cipher Symbol Functions  Based on Homomorphic Encryption Journal of Information Security Reserach    2025, 11 (2): 100-.   Abstract （222）      PDF （1092KB）（158）       Knowledge map    Save Fully homomorphic encryption extends encryption to computations, allowing ciphertext processing without decryption. Comparative operations, crucial in applications like deep learning, pose a challenge in homomorphic encryption environments restricted to addition and multiplication. Feng et al. (CNS 2023) proposed a comparison method using dynamic polynomial combinations. This paper enhances dynamic polynomial, allowing polynomial fluctuations within (-2,2). It introduces a novel equation system for solving dynamic polynomials and utilizes finite third and fifthdegree polynomials to construct more precise composite polynomials for approximating the sign function. It analyzes the method’s optimality in depth consumption and computational complexity, achieving a 32% reduction in runtime compared to the optimal method in a previous study (CNS 2023). The homomorphic comparison algorithm in this paper, for ε=2-20，α=20 requires only 0.69ms in amortized runtime. Reference | Related Articles | Metrics

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Multifamily Malicious Domain Intrusion Detection Based on #br# Collaborative Attention#br# Journal of Information Security Reserach    2024, 10 (12): 115-.   Abstract （218）      PDF （1317KB）（178）       Knowledge map    Save The timely and accurate detection of illegal domain names can effectively prevent the information loss caused by server crashes or unauthorized intrusions. A multifamily malicious domain name intrusion detection method based on collaborative attention is proposed. Firstly, the deep autoencoder network is used to encode and compress layer by layer, extracting the domain name encoding features at the intermediate layer. Secondly, the longdistance and shortdistance encoding features of the domain name string are extracted from the temporal and spatial dimensions, and the selfattention mechanism is constructed on the temporal and spatial encoding feature maps to enhance the expressiveness of the encoding features in local space. Thirdly, the crossattention mechanism is used to establish information interaction between the temporal and spatial encoding features, enhancing the expressiveness of different dimension encoding features in the global space. Finally, the softmax function is used to predict the probability of the domain name to be tested, and quickly determine the legitimacy of the domain name according to the probability value. The results of testing on multiple families of malicious domain name datasets show that the proposed method can achieve a detection accuracy of 0.9876 in the binary classification task of normal and malicious domain names, and an average recognition accuracy of 0.9568 on 16 family datasets. Compared with other classic methods of the same kind, the proposed method achieves the best detection results on multiple evaluation metrics. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 54-.   Abstract （208）      PDF （1425KB）（182）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 139-.   Abstract （197）      PDF （676KB）（148）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on the Development Trend of Cybersecurity Technology Journal of Information Security Reserach    2025, 11 (1): 2-.   Abstract （196）      PDF （563KB）（140）       Knowledge map    Save Related Articles | Metrics

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Stream Cipher Cryptosystem Recognition Scheme Based on Hamming Weight Journal of Information Security Reserach    2024, 10 (12): 1172-.   Abstract （191）      PDF （1655KB）（67）       Knowledge map    Save Based on the known ciphertext, cryptosystem identification is a process of identifying cryptographic algorithms by analyzing the potential feature information in ciphertext data. This paper presents a recognition scheme of sequential cryptosystem based on Hamming weight. This scheme generates labeled ciphertext feature vectors by calculating the Hamming weight of ciphertext blocks of different lengths. LDA dimensionality reduction technique is used to reduce the dimensionality of feature vectors, so as to optimize the extraction and utilization efficiency of data information. Finally, fully connected neural network is used to identify the feature vector after dimensionality reduction. The experimental results show that the proposed scheme can effectively perform two classification recognition experiments and eight classification recognition experiments on 8 stream cipher algorithms such as ZUC, Salsa20 and Decimv2, and achieve good recognition results. The average recognition rate of twoclass and eightclass recognition experiments is 99.29% and 79.12% respectively. Compared with the existing research, the accuracy of this scheme is improved by 16.29% compared with the existing literature with a small amount of ciphertext data. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 207-.   Abstract （189）      PDF （1123KB）（93）       Knowledge map    Save Reference | Related Articles | Metrics

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Blockchainbased Multifactor Crossdomain Authentication Scheme for IoV Journal of Information Security Reserach    2024, 10 (11): 1074-.   Abstract （185）      PDF （4252KB）（105）       Knowledge map    Save With the rapid rise in the number and prevalence of vehicular network (IoV) applications and services, the number of users has continuously increased, making the security of the IoV environment a crucial concern. In IoV systems, there is a risk of vehicle information being stolen or tampered with, which further affects the healthy operation of the system. To address this issue, this paper proposes a blockchainbased crossdomain authentication scheme for IoV. By integrating the entire IoV into a consortium blockchain, the trust gap between different domains is effectively resolved. Multifactor authentication of vehicle user information is employed to effectively prevent information leakage and ensure data security. The combination of blockchain and authentication technologies significantly reduces redundant operations in user identity authentication while enabling synchronized queries of IoV information. From a data security perspective, the security analysis demonstrates the feasibility of this scheme. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 230-.   Abstract （184）      PDF （3359KB）（123）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 114-.   Abstract （183）      PDF （592KB）（98）       Knowledge map    Save Reference | Related Articles | Metrics

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Research on Multimodal Cyberbullying Detection Model for #br# Social Networking Platforms#br# Journal of Information Security Reserach    2025, 11 (2): 154-.   Abstract （180）      PDF （2099KB）（59）       Knowledge map    Save With the rapid development of social networking platforms, the issue of cyberbullying has become increasingly prominent. The diverse forms of online expression that combine text and images have increased the difficulty of detecting and managing cyberbullying. This paper constructs a Chinese multimodal cyberbullying dataset that includes both text and images. By integrating the BERT(bidirectional encoder representations from transformers) model with the ResNet50 model, we extract singlemodal features from text and images, respectively, and perform decisionlevel fusion. The fused features are then detected, achieving accurate identification of text and images as either cyberbullying or noncyberbullying. Experimental results indicate that the multimodal cyberbullying detection model proposed in this paper can effectively identify social media posts or comments that contain cyberbullying characteristics in both text and images. It enhances the practicality, accuracy, and efficiency of detecting multimodal cyberbullying, providing a new approach and method for the detection and management of cyberbullying on social networking platforms. This contributes to the creation of a healthier and more civilized online environment. Reference | Related Articles | Metrics

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Research on Deep Learningbased Spatiotemporal Feature Fusion  Network Intrusion Detection Model Journal of Information Security Reserach    2025, 11 (2): 122-.   Abstract （177）      PDF （1944KB）（164）       Knowledge map    Save As the number of network attacks increases, network intrusion detection systems are becoming increasingly important in maintaining network security. Most studies have used deep learning approaches for network intrusion detection but have not fully utilized the features of traffic from multiple perspectives. Additionally, these studies often suffer from the use of outdated experimental datasets. In this paper, a parallelstructured DSCInceptionBiLSTM network is proposed to evaluate the designed network model using stateoftheart datasets. The model consists of two branches, network traffic image, and text anomaly traffic detection. Spatial and temporal features of traffic are extracted by improved convolutional neural networks and recurrent neural networks, respectively. Finally, network intrusion detection is achieved by fusing spatiotemporal features. The experimental results show that our model achieves 99.96%, 99.19%, and 99.95% accuracy on the three datasets of CICIDS 2017, CSECICIDS 2018 and CICDDoS 2019, respectively, effectively classifying the anomalous traffic with high precision and meeting the requirements of intrusion detection system. Reference | Related Articles | Metrics

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Privacypreserving Federated Learning Research Based on #br# Confused Modulo Projection Homomorphic Encryption#br# Journal of Information Security Reserach    2025, 11 (3): 198-.   Abstract （176）      PDF （1298KB）（132）       Knowledge map    Save In the current era of big data, deep learning is booming and has become a powerful tool for solving realworld problems. However, traditional centralized deep learning systems are at risk of privacy leakage. To address this problem, federated learning, a distributed machine learning approach, has emerged. Federated learning allows multiple organizations or individuals to train models together without sharing raw data, by uploading local model parameters to the server, aggregating each user’s parameters to construct a global model, and returning it to the user. This approach achieves global optimization and avoids private data leakage. However, even with federated learning, attackers may still be able to reconstruct user data by obtaining the model parameters uploaded by users, thus violating  privacy. To address this issue, privacy protection has become the focus of federated learning research. In this paper, we propose a federated learning scheme FLFC (federated learning with confused modulo projection homomorphic encryption) based on confused modulo projection homomorphic encryption to address the above issues. This scheme adopts a selfdeveloped modular fully homomorphic encryption algorithm to encrypt user model parameters. The modular fully homomorphic encryption algorithm has the advantages of high computational efficiency, support for floatingpoint operations, and localization, thus achieving stronger protection of privacy. Experimental results show that the FLFC scheme exhibits a higher average accuracy and good stability compared to the FedAvg scheme in experiments. Reference | Related Articles | Metrics

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A Malicious TLS Traffic Detection Method with Multimodal Features Journal of Information Security Reserach    2025, 11 (2): 130-.   Abstract （175）      PDF （3159KB）（118）       Knowledge map    Save The malicious TLS traffic detection aims to identify network traffic that involves malicious activities transmitted through the TLS protocol. Due to the encryption properties of the TLS protocol, traditional textbased traffic analysis methods have limited effectiveness when dealing with encrypted traffic. To address this issue, a malicious TLS traffic detection method called MultiModal Feature Fusion for TLS Traffic Detection (MTBRL) has been proposed. This method extracts and fuses features from different modalities to detect malicious TLS traffic. Firstly, expert knowledge is employed for feature engineering, extracting key features from encrypted traffic, including protocol versions, encryption suites, and certificate information. These features are processed and transformed into twodimensional image representations. Then, ResNet is utilized to encode these images and extract their features. Simultaneously, an encrypted traffic pretrained BERT model is used to encode TLS flows, allowing the learning of contextual and semantic features of the TLS traffic. Additionally, an LSTM model is employed to encode the sequence of packet length distributions of the encrypted traffic, capturing temporal characteristics. Finally, through feature fusion techniques, the different modality features are integrated, and the model’s weight parameters are automatically learned and optimized using the backpropagation algorithm to accurately predict malicious TLS traffic. Experimental results demonstrate that this method achieves accuracy, precision, recall, and F1score of 94.94%, 94.85%, 94.15%, and 94.45%, on the DataCon2020 dataset. This performance is significantly superior to traditional machine learning and deep learning methods.  Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 49-.   Abstract （174）      PDF （1194KB）（82）       Knowledge map    Save Reference | Related Articles | Metrics

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Design and Implementation of Resourceefficient SM4 Algorithm on FPGA Journal of Information Security Reserach    2025, 11 (6): 490-.   Abstract （171）      PDF （2238KB）（85）       Knowledge map    Save In the hardware implementation of the SM4 algorithm, the lookup table method is commonly adopted for realizing the Sbox, which consumes a significant amount of hardware resources. This paper proposes an implementation scheme for the SM4 algorithm based on polynomial basis. Two construction schemes are developed for the 8×8 Sbox used in the SM4 algorithm, one based on composite field GF((24)2) and the other on composite field GF(((22)2)2). The test results indicate that the scheme based on polynomial bases GF((24)2) is optimal. Taking into account both resource utilization and performance, this paper designs two hardware implementation structures for SM4: a state machine parallel structure and a pipelined structure. Compared with the traditional lookup table approach, the state machine parallel structure reduces resource utilization by 21.98% while increasing the operating frequency by 14.4%. The pipelined structure achieves a reduction in resource utilization by 54.23%. Reference | Related Articles | Metrics

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Traffic Anomaly Detection Method by Secondorder Feature  Journal of Information Security Reserach    2024, 10 (12): 1082-.   Abstract （170）      PDF （2415KB）（139）       Knowledge map    Save A method is proposed to address the challenge of low detection rates for minority class attack traffic in deep learning models when dealing with imbalanced massive highdimensional network traffic data. Firstly, the isolation forest (iForest) is employed to remove outliers from normal class samples, used for training an enhanced Convolutional Denoising Autoencoder (CDAE) to mitigate the impact of noise and outliers on model training, resulting in a lowdimensional enhanced representation of the original features. Secondly, leveraging ADASYN on the outlierfree dataset to synthetically generate minority class attack samples, thereby resolving the data imbalance issue. Subsequently, using iForest to clean the newly generated samples from outliers, a new dataset is obtained. Employing the pretrained CDAE on this dataset achieves a firstround feature extraction, and the extracted features serve as input for a selfdistilled ResNet model to perform secondorder feature extraction. Finally, precise identification of anomalous traffic is accomplished by combining the trained CDAE and ResNet models. The method achieves the highest fiveclass accuracy and F1 score of 91.52% and 92.05%, respectively, on the NSLKDD dataset. Experimental results demonstrate that, compared to existing methods, this approach effectively enhances the detection rates for minority class attack traffic. Reference | Related Articles | Metrics

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An Adaptive Network Attack Analysis Method Based on Federated Learning Journal of Information Security Reserach    2024, 10 (12): 1091-.   Abstract （169）      PDF （3389KB）（153）       Knowledge map    Save To analyze network attack behavior issues efficiently and securely, an adaptive network attack analysis method based on federated learning (NAAFL) is proposed. This approach can fully leverage data for network attack analysis while ensuring privacy protection.. Firstly, a costeffective defense mechanism based on DQN (dynamic participant selection mechanism) is proposed to act in the process of federated learning model parameter sharing and model aggregation. It dynamically selects the best participants for each round of model updates, reducing the impact of poorly performing local models on the global model during training. It also reduces communication overhead time and improving the efficiency of federated learning. Secondly, an adaptive feature learning network intrusion detection model is designed, which is able to intelligently learn and analyze according to changing attack features to cope with complex network environments. It effectively reduces the time and space overhead of feature selection. Finally, comparative experiment is performed on a public data set (NSL KDD). The NAAFL method detects attacks with an accuracy of 98.9%. Dynamically selecting participants increases server accuracy by 4.48%. The experimental results show that the method has excellent robustness and efficiency. Reference | Related Articles | Metrics

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A Federated Learning Method Resistant to Label Flip Attack Journal of Information Security Reserach    2025, 11 (3): 205-.   Abstract （166）      PDF （3486KB）（78）       Knowledge map    Save Since users participating in federated learning training have high autonomy and their identities are difficult to identify, they are vulnerable to label flip attacks, causing the model to learn wrong rules from wrong labels and reducing the overall performance of the model. In order to effectively resist label flip attacks, a dilutionprotected federated learning method for multistage training models is proposed. This method randomly divides the training data set and uses a dilution protection federated learning algorithm to distribute part of the data to clients participating in the training to limit the amount of data owned by the client and avoid malicious participants with large amounts of data from causing major damage to the model. After each training session, the gradients of all training epochs in that phase are gradient clustered by a dimensionality reduction algorithm in order to identify potentially malicious actors and restrict their training in the next phase. At the same time, the global model parameters are saved after each stage of training to ensure that the training of each stage is based on the model foundation of the previous stage. Experimental results on the data set show that this method reduces the impact of attacks without damaging the model accuracy, and helps improve the convergence speed of the model. Reference | Related Articles | Metrics

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Dynamic Searchable Encryption Scheme Supporting Fuzzy Multiple Keywords Journal of Information Security Reserach    2024, 10 (11): 1064-.   Abstract （162）      PDF （2035KB）（67）       Knowledge map    Save With the development of cloud computing, the convenience and costeffectiveness of cloud storage have caused a large number of users to store personal data on thirdparty cloud servers. While encrypted storage of data in the cloud ensures data security, it also introduces challenges in data retrieval. Dynamic symmetric searchable encryption technology emerged as the times require, which not only effectively protects data privacy but also enables multikeyword joint search functions. Additionally, the fuzzy search introduced by this technology in practical applications enhances the user’s search experience and improves search efficiency. However, the current searchable encryption schemes that supports fuzzy search has certain security risks and do not adequately address potential information leakage issues during dynamic updates. To tacklethese issues, this paper proposes a dynamic searchable encryption scheme that supports fuzzy multikeywords, ensuring information security during dynamic updates, and also supporting multikeyword joint search and fuzzy search. This scheme designs a keyword encoding algorithm and localitysensitive hash function to build a fuzzy index, and uses a Bloom filter encryption algorithm to encrypt the index to achieve fuzzy search. Furthermore, a trusted execution environment is introduced to reduce the communication overhead and computing overhead as well as the number of interactions between users and servers. Finally, the safety and effectiveness of this scheme were verified through experiments. Reference | Related Articles | Metrics

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Research and Analysis of Named Entity Recognition Technology in #br# Threat Intelligence#br# #br# Journal of Information Security Reserach    2024, 10 (12): 1122-.   Abstract （160）      PDF （990KB）（130）       Knowledge map    Save In the face of increasingly complex network security attacks, it is very important to quickly obtain the latest network threat intelligence for realtime identification, blocking and tracking of network attacks. The key to solve this problem is how to obtain network threat intelligence data effectively, and named entity recognition technology is one of the hot technologies to solving this problem. This paper systematically analyzes several named entity recognition methods based on deep learning, and then designs a named entity recognition model suitable for threat intelligence field, and carries out experimental verification and analysis. Finally, the challenges faced by named entity recognition methods and their development prospects in the field of network security are analyzed and prospected. Reference | Related Articles | Metrics

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A Survey of Network Topology Obfuscation Techniques Journal of Information Security Reserach    2025, 11 (4): 296-.   Abstract （158）      PDF （1248KB）（114）       Knowledge map    Save LinkFlooding Attack (LFA) is a novel distributed denialofservice (DDoS) attack that exploits network topology detection. Network Topology Obfuscation serves as an effective deceptive defense mechanism against this attack, aiming to provide proactive protection before an attack occurs. Over the past decade, relevant research has continuously made progress, proposing corresponding obfuscation solutions for different scenarios and objectives. This paper comprehensively reviews the network topology obfuscation techniques. First, it combines the basic principles and classifications of network topology discovery to point out the risks of topology leakage in current network topology discovery. Next, it formally defines network topology obfuscation design and presents a proactive defense model. Then, based on the obfuscation concept, the technologies are divided into packet modification, decoy traps, routing mutation, and metric forgery schemes, and proposes a set of metrics to comprehensively compare the current mainstream network topology obfuscation techniques. Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 18-.   Abstract （158）      PDF （1387KB）（69）       Knowledge map    Save Reference | Related Articles | Metrics

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Journal of Information Security Reserach    2024, 10 (E2): 249-.   Abstract （152）      PDF （687KB）（88）       Knowledge map    Save Reference | Related Articles | Metrics