IEEE Transactions on Information Forensics and Security最新文献_第6页

Byzantine Fault Tolerance With Non-Determinism, Revisited 拜占庭容错与非确定性再探讨

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516541

Yue Huang;Huizhong Li;Yi Sun;Sisi Duan

{"title":"Byzantine Fault Tolerance With Non-Determinism, Revisited","authors":"Yue Huang;Huizhong Li;Yi Sun;Sisi Duan","doi":"10.1109/TIFS.2024.3516541","DOIUrl":"10.1109/TIFS.2024.3516541","url":null,"abstract":"Conventional Byzantine fault tolerance (BFT) requires replicated state machines to execute deterministic operations only. In practice, numerous applications and scenarios, especially in the era of blockchains, contain various sources of non-determinism. Meanwhile, it is even sometimes desirable to support non-determinism, and replicas still agree on the execution results. Despite decades of research on BFT, we still lack an efficient and easy-to-deploy solution for BFT with non-determinism—BFT-ND, especially in the asynchronous setting. We revisit the problem of BFT-ND and provide a formal and asynchronous treatment of BFT-ND. In particular, we design and implement Block-ND that insightfully separates the task of agreeing on the order of transactions from the task of agreement on the state: Block-ND allows reusing existing BFT implementations; on top of BFT, we reduce the agreement on the state to multivalued Byzantine agreement (MBA), a somewhat neglected primitive by practical systems. Block-ND is completely asynchronous as long as the underlying BFT is asynchronous. We provide a new MBA construction that is significantly faster than existing MBA constructions. We instantiate Block-ND in both the partially synchronous setting (with PBFT, OSDI 1999) and the purely asynchronous setting (with PACE, CCS 2022). Via a 91-instance WAN deployment on Amazon EC2, we show that Block-ND has only marginal performance degradation compared to conventional BFT.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"309-322"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Finger Valley Points-Free ROI Detection and Recurrent Layer Aggregation for Palmprint Recognition in Open Environment 联合指谷无点 ROI 检测和递归层聚合技术用于开放环境中的掌纹识别

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516539

Tingting Chai;Xin Wang;Ru Li;Wei Jia;Xiangqian Wu

{"title":"Joint Finger Valley Points-Free ROI Detection and Recurrent Layer Aggregation for Palmprint Recognition in Open Environment","authors":"Tingting Chai;Xin Wang;Ru Li;Wei Jia;Xiangqian Wu","doi":"10.1109/TIFS.2024.3516539","DOIUrl":"10.1109/TIFS.2024.3516539","url":null,"abstract":"Cooperative palmprint recognition, pivotal for civilian and commercial uses, stands as the most essential and broadly demanded branch in biometrics. These applications, often tied to financial transactions, require high accuracy in recognition. Currently, research in palmprint recognition primarily aims to enhance accuracy, with relatively few studies addressing the automatic and flexible palm region of interest (ROI) extraction (PROIE) suitable for complex scenes. Particularly, the intricate conditions of open environment, alongside the constraint of human finger skeletal extension limiting the visibility of Finger Valley Points (FVPs), render conventional FVPs-based PROIE methods ineffective. In response to this challenge, we propose an FVPs-Free Adaptive ROI Detection (FFARD) approach, which utilizes cross-dataset hand shape semantic transfer (CHSST) combined with the constrained palm inscribed circle search, delivering exceptional hand segmentation and precise PROIE. Furthermore, a Recurrent Layer Aggregation-based Neural Network (RLANN) is proposed to learn discriminative feature representation for high recognition accuracy in both open-set and closed-set modes. The Angular Center Proximity Loss (ACPLoss) is designed to enhance intra-class compactness and inter-class discrepancy between learned palmprint features. Overall, the combined FFARD and RLANN methods are proposed to address the challenges of palmprint recognition in open environment, collectively referred to as RDRLA. Experimental results on four palmprint benchmarks HIT-NIST-V1, IITD, MPD and BJTU_PalmV2 show the superiority of the proposed method RDRLA over the state-of-the-art (SOTA) competitors. The code of the proposed method is available at \u0000<uri>https://github.com/godfatherwang2/</uri>\u0000 RDRLA.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"421-435"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Global or Local Adaptation? Client-Sampled Federated Meta-Learning for Personalized IoT Intrusion Detection

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516548

Haorui Yan;Xi Lin;Shenghong Li;Hao Peng;Bo Zhang

{"title":"Global or Local Adaptation? Client-Sampled Federated Meta-Learning for Personalized IoT Intrusion Detection","authors":"Haorui Yan;Xi Lin;Shenghong Li;Hao Peng;Bo Zhang","doi":"10.1109/TIFS.2024.3516548","DOIUrl":"10.1109/TIFS.2024.3516548","url":null,"abstract":"With the increasing size of Internet of Things (IoT) devices, cyber threats to IoT systems have increased. Federated learning (FL) has been implemented in an anomaly-based intrusion detection system (NIDS) to detect malicious traffic in IoT devices and counter the threat. However, current FL-based NIDS mainly focuses on global model performance and lacks personalized performance improvement for local data. To address this issue, we propose a novel personalized federated meta-learning intrusion detection approach (PerFLID), which allows multiple participants to personalize their local detection models for local adaptation. PerFLID shifts the goal of the personalized detection task to training a local model suitable for the client’s specific data, rather than a global model. To meet the real-time requirements of NIDS, PerFLID further refines the client selection strategy by clustering the local gradient similarities to find the nodes that contribute the most to the global model per global round. PerFLID can select the nodes that accelerate the convergence of the model, and we theoretically analyze the improvement in the convergence speed of this strategy over the personalized federated learning algorithm. We experimentally evaluate six existing FL-NIDS approaches on three real network traffic datasets and show that our PerFLID approach outperforms all baselines in detecting local adaptation accuracy by 10.11% over the state-of-the-art scheme, accelerating the convergence speed under various parameter combinations.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"279-293"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NMFAD: Neighbor-Aware Mask-Filling Attributed Network Anomaly Detection NMFAD：邻居感知掩码填充归因网络异常现象检测

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516570

Liang Xi;Runze Li;Menghan Li;Dehua Miao;Ruidong Wang;Zygmunt J. Haas

{"title":"NMFAD: Neighbor-Aware Mask-Filling Attributed Network Anomaly Detection","authors":"Liang Xi;Runze Li;Menghan Li;Dehua Miao;Ruidong Wang;Zygmunt J. Haas","doi":"10.1109/TIFS.2024.3516570","DOIUrl":"10.1109/TIFS.2024.3516570","url":null,"abstract":"As a widely adopted protocol for anomaly detection in attributed networks, reconstruction error prioritizes comprehensive feature extraction to detect anomalies over interrogating the differential representation between normal and abnormal nodes. Intuitively, in attributed networks, normal nodes and their neighbors often exhibit similarities, whereas abnormal nodes demonstrate behaviors distinct from their neighbors. Hence, normal nodes can be accurately represented through their neighbors and effectively reconstructed. As opposed to normal nodes, abnormal nodes represented by their neighbors may be erroneously reconstructed as normal, resulting in increased reconstruction error. Leveraging from this observation, we propose a novel anomaly detection protocol called Neighbor-aware Mask-Filling Anomaly Detection (NMFAD) for attributed networks, aiming to maximize the variability between original and reconstructed features of abnormal nodes filled with information from their neighbors. Specifically, we utilize random-mask on nodes and integrate them into the backbone Graph Neural Networks (GNNs) to map nodes into a latent space. Subsequently, we fill the masked nodes with embeddings from their neighbors and smooth the abnormal nodes closer to the distribution of normal nodes. This optimization improves the likelihood of the decoder to reconstructing abnormal nodes as normal, thereby maximizing the reconstruction error of abnormal nodes. Experimental results demonstrate that, compared to the existing models, NMFAD exhibits superior performance.in attributed networks.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"364-374"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Tracking Control and Attack Detection for Power Cyber-Physical Systems based on Integrated Control Decision 基于综合控制决策的电力网络物理系统安全跟踪控制与攻击检测

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/tifs.2024.3516557

Chaowei Sun, Qingyu Su, Jian Li

引用次数: 0

Fed-UGI: Federated Undersampling Learning Framework with Gini Impurity for Imbalanced Network Intrusion Detection

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/tifs.2024.3516547

Ming Zheng, Xiaowen Hu, Ying Hu, Xiaoyao Zheng, Yonglong Luo

引用次数: 0

Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection Under Noisy Annotations 增强的重要性：噪声注释下的 X 射线违禁品检测混合粘贴法

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516546

Ruikang Chen;Yan Yan;Jing-Hao Xue;Yang Lu;Hanzi Wang

{"title":"Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection Under Noisy Annotations","authors":"Ruikang Chen;Yan Yan;Jing-Hao Xue;Yang Lu;Hanzi Wang","doi":"10.1109/TIFS.2024.3516546","DOIUrl":"10.1109/TIFS.2024.3516546","url":null,"abstract":"Automatic X-ray prohibited item detection is vital for public safety. Existing deep learning-based methods all assume that the annotations of training X-ray images are correct. However, obtaining correct annotations is extremely hard if not impossible for large-scale X-ray images, where item overlapping is ubiquitous. As a result, X-ray images are easily contaminated with noisy annotations, leading to performance deterioration of existing methods. In this paper, we address the challenging problem of training a robust prohibited item detector under noisy annotations (including both category noise and bounding box noise) from a novel perspective of data augmentation, and propose an effective label-aware mixed patch paste augmentation method (Mix-Paste). Specifically, for each item patch, we mix several item patches with the same category label from different images and replace the original patch in the image with the mixed patch. In this way, the probability of containing the correct prohibited item within the generated image is increased. Meanwhile, the mixing process mimics item overlapping, enabling the model to learn the characteristics of X-ray images. Moreover, we design an item-based large-loss suppression (LLS) strategy to suppress the large losses corresponding to potentially positive predictions of additional items due to the mixing operation. We show the superiority of our method on X-ray datasets under noisy annotations. In addition, we evaluate our method on the noisy MS-COCO dataset to showcase its generalization ability. These results clearly indicate the great potential of data augmentation to handle noise annotations. The source code is released at \u0000<uri>https://github.com/wscds/Mix-Paste</uri>\u0000.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"234-248"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EASNs: Efficient Anonymous Social Networks With Enhanced Security and High Scalability

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516568

Wenfeng Huang;Axin Wu;Shengmin Xu;Guowen Xu;Wei Wu

{"title":"EASNs: Efficient Anonymous Social Networks With Enhanced Security and High Scalability","authors":"Wenfeng Huang;Axin Wu;Shengmin Xu;Guowen Xu;Wei Wu","doi":"10.1109/TIFS.2024.3516568","DOIUrl":"10.1109/TIFS.2024.3516568","url":null,"abstract":"Privacy concerns have been persistently afflicting individuals within online social networks (OSNs), rendering privacy-preserving communications over the Internet with authentication especially important. Unfortunately, the guarantees of privacy and authenticity are not always provided in OSNs. Individuals are still facing the challenges of being deceived or exploited. To mitigate these issues, anonymous social networks (ASNs) have emerged as a remedy for OSNs, facilitating individuals to connect with others anonymously and authentically. Despite the existence of numerous and remarkable cryptographic primitives, there are no formal solutions for ASNs except for matchmaking encryption (ME), since ME can simultaneously provide various key functionalities, i.e. bilateral access control, identity anonymity, and message authentication, to address the requirements of ASNs. In this paper, we design a system for ASNs by adopting fuzzy identity-based matchmaking encryption (fuzzy IB-ME), and the proposed scheme in this work is highly efficient. The scheme also realizes adaptive security in generic group model (GGM), which is generally adopted in pairing-based cryptography. The proposed ASNs system offers various advantages compared to the previous solutions, including 1) bilateral access control, 2) enhanced security, 3) high scalability, and 4) high efficiency. In addition to theoretical evaluations, we conduct extensive experiments to evaluate our scheme’s computational and storage efficiency. These evaluations indicate that our solution outperforms previous solutions and as well as preserves many desired functionalities.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"796-806"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications SCU：深度学习支持语义通信的高效机器解除学习方案

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/TIFS.2024.3516576

Weiqi Wang;Zhiyi Tian;Chenhan Zhang;Shui Yu

{"title":"SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications","authors":"Weiqi Wang;Zhiyi Tian;Chenhan Zhang;Shui Yu","doi":"10.1109/TIFS.2024.3516576","DOIUrl":"10.1109/TIFS.2024.3516576","url":null,"abstract":"Deep learning (DL) enabled semantic communications leverage DL to train encoders and decoders (codecs) to extract and recover semantic information. However, most semantic training datasets contain personal private information. Such concerns call for enormous requirements for specified data erasure from semantic codecs when previous users hope to move their data from the semantic system. Existing machine unlearning solutions remove data contribution from trained models, yet usually in supervised sole model scenarios. These methods are infeasible in semantic communications that often need to jointly train unsupervised encoders and decoders. In this paper, we investigate the unlearning problem in DL-enabled semantic communications and propose a semantic communication unlearning (SCU) scheme to tackle the problem. SCU includes two key components. Firstly, we customize the joint unlearning method for semantic codecs, including the encoder and decoder, by minimizing mutual information between the learned semantic representation and the erased samples. Secondly, to compensate for semantic model utility degradation caused by unlearning, we propose a contrastive compensation method, which considers the erased data as the negative samples and the remaining data as the positive samples to retrain the unlearned semantic models contrastively. Theoretical analysis and extensive experimental results on three representative datasets demonstrate the effectiveness and efficiency of our proposed methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"547-558"},"PeriodicalIF":6.3,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142815911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-Preserving Generative Modeling with Sliced Wasserstein Distance

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-12-12 DOI: 10.1109/tifs.2024.3516549

Ziniu Liu, Han Yu, Kai Chen, Aiping Li

引用次数: 0