IEEE Transactions on Information Forensics and Security最新文献

{"title":"RLP-ABE: Puncturable CP-ABE for Efficient User Revocation From Lattices in Cloud Storage","authors":"Mengxue Yang;Huaqun Wang;Debiao He;Jiankuo Dong","doi":"10.1109/TIFS.2025.3613055","DOIUrl":"10.1109/TIFS.2025.3613055","url":null,"abstract":"Cloud computing has become the predominant platform for data sharing due to its adaptability, cost-effectiveness, and ability to scale resources according to user demand. Ensuring secure and efficient data sharing has long been a central research focus, with attribute-based encryption (ABE) serving as a key cryptographic primitive. In real-world scenarios, user attributes often change, necessitating timely revocation of access rights. Common user revocation methods include direct and indirect revocation. Direct revocation is controlled by the data owner, who adds revocation information to a list and embeds it into ciphertext to revoke permissions. Indirect revocation is managed by an authorized authority or delegated third party, dynamically publishing revocation information and generating new keys and ciphertexts. Conventional direct and indirect revocation methods incur substantial communication and computation overheads, limiting their practical effectiveness, particularly in environments with frequent user access terminations. To address these challenges, we propose a novel puncturable ciphertext-policy ABE scheme based on lattice cryptography for user revocation, eliminating the need for key regeneration and revocation-list maintenance. The proposed approach effectively resists collusion, quantum, and chosen-plaintext attacks, and experimental evaluations demonstrate its advantages in storage consumption, communication cost, and computational overhead.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"10219-10230"},"PeriodicalIF":8.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116688","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}

{"title":"FedAMM: Federated Learning Against Majority Malicious Clients Using Robust Aggregation","authors":"Keke Gai;Dongjue Wang;Jing Yu;Liehuang Zhu;Weizhi Meng","doi":"10.1109/TIFS.2025.3607273","DOIUrl":"10.1109/TIFS.2025.3607273","url":null,"abstract":"As a collaborative framework designed to safeguard privacy, <italic>Federated Learning</i> (FL) seeks to protect participants’ data throughout the training process. However, the framework still faces security risks from poisoning attacks, arising from the unmonitored process of client-side model updates. Most existing solutions address scenarios where less than half of clients are malicious, i.e., which leaves a significant challenge to defend against attacks when more than half of partici pants are malicious. In this paper, we propose a FL scheme, named FedAMM, that resists backdoor attacks across various data distributions and malicious client ratios. We develop a novel backdoor defense mechanism to filter out malicious models, aiming to reduce the performance degradation of the model. The proposed scheme addresses the challenge of distance measurement in high-dimensional spaces by applying <italic>Principal Component Analysis</i> (PCA) to improve clustering effectiveness. We borrow the idea of critical parameter analysis to enhance discriminative ability in non-iid data scenarios, via assessing the benign or malicious nature of models by comparing the similarity of critical parameters across different models. Finally, our scheme employs a hierarchical noise perturbation to improve the backdoor mitigation rate, effectively eliminating the backdoor and reducing the adverse effects of noise on task accuracy. Through evaluations conducted on multiple datasets, we demonstrate that the proposed scheme achieves superior backdoor defense across diverse client data distributions and different ratios of malicious participants. With 80% malicious clients, FedAMM achieves low backdoor attack success rates of 1.14%, 0.28%, and 5.53% on MNIST, FMNIST, and CIFAR-10, respectively, demonstrating enhanced robustness of FL against backdoor attacks.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"9950-9964"},"PeriodicalIF":8.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127885","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}

{"title":"Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics","authors":"Jane Downer, Ren Wang, Binghui Wang","doi":"10.1109/tifs.2025.3613061","DOIUrl":"https://doi.org/10.1109/tifs.2025.3613061","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"30 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127662","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}

{"title":"Differentially-Private Collaborative Online Personalized Mean Estimation","authors":"Yauhen Yakimenka, Chung-Wei Weng, Hsuan-Yin Lin, Eirik Rosnes, Jörg Kliewer","doi":"10.1109/tifs.2025.3612276","DOIUrl":"https://doi.org/10.1109/tifs.2025.3612276","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"40 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116686","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}

{"title":"Using Random Forests for Efficient Identification of Decoys Under Link Flooding Attacks in SDNs","authors":"Wenjie Yu, Boyang Zhou","doi":"10.1109/tifs.2025.3612159","DOIUrl":"https://doi.org/10.1109/tifs.2025.3612159","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"1 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089371","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}

{"title":"Detection of Unknown Attacks Through Encrypted Traffic: A Gaussian Prototype-Aided Variational Autoencoder Framework","authors":"Qianwei Meng, Jing Tao, Qingjun Yuan, Guangsong Li, Yongjuan Wang, Bing Gao, Siqi Lu","doi":"10.1109/tifs.2025.3612141","DOIUrl":"https://doi.org/10.1109/tifs.2025.3612141","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"11 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089373","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}

{"title":"Optimal String Sanitization Against Strategic Attackers","authors":"Pengxin Bian, George Theodorakopoulos, Solon P. Pissis, Grigorios Loukides","doi":"10.1109/tifs.2025.3610245","DOIUrl":"https://doi.org/10.1109/tifs.2025.3610245","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"162 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089370","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}

{"title":"JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Examples","authors":"Benedetta Tondi, Wei Guo, Niccolò Pancino, Mauro Barni","doi":"10.1109/tifs.2025.3611121","DOIUrl":"https://doi.org/10.1109/tifs.2025.3611121","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"36 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089372","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}

{"title":"Fine-Grained and Class-Incremental Malicious Account Detection in Ethereum via Dynamic Graph Learning","authors":"Hanbiao Du;Meng Shen;Yang Liu;Zheng Che;Jinhe Wu;Wei Wang;Liehuang Zhu","doi":"10.1109/TIFS.2025.3612194","DOIUrl":"10.1109/TIFS.2025.3612194","url":null,"abstract":"Ethereum serves as the cornerstone for value transfer in Web 3.0, providing a decentralized and efficient trust mechanism for global connectivity. However, the anonymity of Ethereum undermines market regulatory capabilities, leading to frequent malicious behaviors such as Ponzi Scheme, Money Laundering, and Phishing. Therefore, in the face of the diverse and continuously emerging malicious behaviors, implementing fine-grained detection is crucial for maintaining the prosperous development of the blockchain ecosystem. In this paper, we propose FiMAD, a fine-grained and class-incremental malicious account detection framework based on dynamic graph learning. Specifically, we first propose a general graph structure called Dynamic Account Relation Graph (DARG), which dynamically models Ethereum accounts from a continuous-time perspective. Then, we design a cascade graph feature extraction method to capture deep temporal evolution patterns and neighbor interaction features in DARG. Next, we construct a pre-training universal encoder to transform account features into high-dimensional embeddings, followed by fine-tuning the model classifier with a few labeled samples, enabling accurate fine-grained detection and rapid updates for incremental classes. We conduct extensive experiments using real Ethereum data. The results demonstrate that FiMAD outperforms state-of-the-art (SOTA) methods in fine-grained detection across five typical scenarios: class-incremental, full data, new malicious accounts, imbalanced data, and binary classification. In the class-incremental scenario, FiMAD improves the Macro-F1 by up to 26.4% compared to SOTA methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"10130-10145"},"PeriodicalIF":8.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089506","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}

{"title":"LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding","authors":"Yifan Jia;Yanbin Wang;Jianguo Sun;Ye Tian;Peng Qian","doi":"10.1109/TIFS.2025.3612149","DOIUrl":"10.1109/TIFS.2025.3612149","url":null,"abstract":"As Ethereum confronts increasingly sophisticated fraud threats, recent research seeks to improve fraud account detection by leveraging advanced pre-trained Transformer or self-supervised graph neural network. However, current Transformer-based methods rely on context-independent, numerical transaction sequences, failing to capture semantic of account transactions. Furthermore, the pervasive homogeneity in Ethereum transaction records renders it challenging to learn discriminative account embeddings. Moreover, current self-supervised graph learning methods primarily learn node representations through graph reconstruction, resulting in suboptimal performance for node-level tasks like fraud account detection, while these methods also encounter scalability challenges. To tackle these challenges, we propose LMAE4Eth, a multi-view learning framework that fuses transaction semantics, masked graph embedding, and expert knowledge. We first propose a transaction-token contrastive language model (TxCLM) that transforms context-independent numerical transaction records into logically cohesive linguistic representations, and leverages language modeling to learn transaction semantics. To clearly characterize the semantic differences between accounts, we also use a token-aware contrastive learning pre-training objective, which, together with the masked transaction model pre-training objective, learns high-expressive account representations. We then propose a masked account graph autoencoder (MAGAE) using generative self-supervised learning, which achieves superior node-level account detection by focusing on reconstructing account node features rather than graph structure. To enable MAGAE to scale for large-scale training, we propose to integrate layer-neighbor sampling into the graph, which reduces the number of sampled vertices by several times without compromising training quality. Additionally, we initialize the account nodes in the graph with expert-engineered features to inject empirical and statistical knowledge into the model. Finally, using a cross-attention fusion network, we unify the embeddings of TxCLM and MAGAE to leverage the benefits of both. We evaluate our method against 21 baseline approaches on three datasets. Experimental results show that our method improves the F1-score by over 10% at most compared with the best baseline. Furthermore, we observe from three datasets that the proposed method demonstrates strong generalization ability compared to previous work. Our source code is avaliable at: <uri>https://github.com/lmae4eth/LMAE4Eth</uri>","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"10260-10274"},"PeriodicalIF":8.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089369","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}