IEEE Transactions on Information Forensics and Security最新文献_第4页

Invariant Correlation of Representation With Label 标记表示的不变性相关

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-17 DOI: 10.1109/TIFS.2025.3562031

Gaojie Jin;Ronghui Mu;Xinping Yi;Xiaowei Huang;Lijun Zhang

{"title":"Invariant Correlation of Representation With Label","authors":"Gaojie Jin;Ronghui Mu;Xinping Yi;Xiaowei Huang;Lijun Zhang","doi":"10.1109/TIFS.2025.3562031","DOIUrl":"10.1109/TIFS.2025.3562031","url":null,"abstract":"The Invariant Risk Minimization (IRM) approach aims to address the security challenge of out-of-distribution robustness (domain generalization) by training a feature representation that remains invariant across multiple environments. However, in noisy environments, noise can distort invariant features, leading to different environment-specific losses. Current IRM-related methods such as IRMv1 and VREx underperform in these settings because they enforce uniform losses across environments. While environmental noise causes environment-specific losses, it does not alter the fundamental correlation between invariant representations and labels. Based on this observation, we propose ICorr (Invariant Correlation), which leverages this correlation to extract invariant representations in noisy settings. Unlike existing approaches, ICorr accommodates different environment-specific inherent losses while maintaining a necessary condition for identifying IRM classifiers. We present a detailed case study demonstrating why previous methods may lose ground while ICorr can succeed. Through a theoretical lens, particularly from a causality perspective, we illustrate that the invariant correlation of representation with label is a necessary condition for the optimal invariant predictor in noisy environments, where as the optimization motivations for other methods may not be. Furthermore, we empirically demonstrate the effectiveness of ICorr by comparing it with other domain generalization methods on various noisy datasets.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4369-4381"},"PeriodicalIF":6.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847068","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

Structurally-Encrypted Databases Combined with Filters: Enhanced Security and Rich Queries 结合过滤器的结构化加密数据库：增强的安全性和丰富的查询

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-16 DOI: 10.1109/tifs.2025.3561680

Feng Liu, Jinjiang Yang, Jingcheng Zhao, Yingjie Xue, Kaiping Xue

引用次数: 0

Towards Unsupervised Time-Series Anomaly Detection for Virtual Cloud Networks 面向虚拟云网络的无监督时间序列异常检测

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-16 DOI: 10.1109/TIFS.2025.3561672

Zixuan Ma;Chen Li;Kun Zhang;Bibo Tu

{"title":"Towards Unsupervised Time-Series Anomaly Detection for Virtual Cloud Networks","authors":"Zixuan Ma;Chen Li;Kun Zhang;Bibo Tu","doi":"10.1109/TIFS.2025.3561672","DOIUrl":"10.1109/TIFS.2025.3561672","url":null,"abstract":"Virtual cloud network (VCN) is a fundamental cloud resource for endpoints (VMs or containers) to communicate with each other and with the outside. Anomaly detection, a key security approach for VCNs, faces serious challenges: 1) Current feature models are difficult to apply to VCNs with significant differences from traditional networks. 2) Current anomaly detection models lack the adaptability to learn multiple normal patterns simultaneously. The need to train a dedicated model for each endpoint causes serious scalability problems in VCNs. 3) Current anomaly detection models have difficulty addressing the complex temporal dependency and non-stationarity of VCNs. To address these challenges, we propose a new multilevel feature model MFM and a new unsupervised time-series anomaly detection model GTGmVAE. By combining the basic features with the topology features specifically designed for VCNs, MFM effectively characterizes the patterns of VCNs. GTGmVAE combines the new local-global feature extractor with the latent space following a Gaussian mixture distribution to achieve the strong adaptability to learn multiple normal patterns simultaneously, and achieves the strong temporal modeling capability to effectively address the complex temporal dependency and non-stationarity of VCNs by adequately modeling the global temporal dependencies of the input samples and latent variables. Extensive experiments on the VCN anomaly detection dataset CIC-IDS2018 and the time-series anomaly detection benchmark dataset SMD show that GTGmVAE with MFM achieves the desirable performance, and GTGmVAE outperforms all nine representative state-of-the-art detection models.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4322-4337"},"PeriodicalIF":6.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841565","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

CorreFlow: A Covert Fingerprinting Modulation for Flow Correlation in Open Heterogeneous Networks CorreFlow：开放异构网络中流量相关的隐蔽指纹调制

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-16 DOI: 10.1109/tifs.2025.3561681

Yue Chen, Xiaohui Li, Junfeng Wang, Wenhan Ge, Lingfeng Tan

引用次数: 0

DP-DID: A Dynamic and Proactive Decentralized Identity System DP-DID：一个动态和主动的去中心化身份系统

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-16 DOI: 10.1109/tifs.2025.3561662

Jie Yin, Yang Xiao, Qian Chen, Yongzhi Lim, Xuefeng Liu, Qingqi Pei, Jianying Zhou

引用次数: 0

A Multi-Granularity Deep Signal Shrinkage Network for Noise-Robust Specific Emitter Identification 一种多粒度深度信号收缩网络用于噪声鲁棒特定发射器识别

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-15 DOI: 10.1109/TIFS.2025.3560690

Guangjie Han;Weitao Wang;Zhengwei Xu

{"title":"A Multi-Granularity Deep Signal Shrinkage Network for Noise-Robust Specific Emitter Identification","authors":"Guangjie Han;Weitao Wang;Zhengwei Xu","doi":"10.1109/TIFS.2025.3560690","DOIUrl":"10.1109/TIFS.2025.3560690","url":null,"abstract":"Wireless network security is a significant issue in wireless communication systems. Specific emitter identification (SEI) technology, as an effective physical layer authentication method, has been extensively studied. Methods based on deep learning (DL) for SEI have emerged as the predominant approach, attributed to their end-to-end recognition framework and enhanced capability for feature extraction. However, the training of DL models relies on high-quality data, and the data collection in real-world scenarios is often in low signal-to-noise ratio (SNR) environments, leading to poor model training performance. This paper presents a novel solution, the Multi-Granularity Deep Signal Shrinkage Network (MGDSSN), for the challenging task of SEI in low SNR environments. To this end, the proposed MGDSSN incorporates soft thresholding processing and employs subnetworks for adaptive thresholding, effectively eliminating noise-related features and achieving robust SEI in low SNR environments. Additionally, MGDSSN incorporates a multi-granularity deep signal network architecture that improves the recognition accuracy and stability of the model. This is achieved by capturing the interrelated attributes of in-phase/quadrature-phase (I/Q) signals and features at multiple levels of granularity. Experiments conducted with real-world dataset reveal that the proposed MGDSSN surpasses the current state-of-the-art SEI methods in low SNR environments, demonstrating robust SEI and verifying the superiority of the proposed method.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4256-4264"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836843","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

Robust Detection of Malicious Encrypted Traffic via Contrastive Learning 基于对比学习的恶意加密流量鲁棒检测

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-15 DOI: 10.1109/TIFS.2025.3560560

Meng Shen;Jinhe Wu;Ke Ye;Ke Xu;Gang Xiong;Liehuang Zhu

{"title":"Robust Detection of Malicious Encrypted Traffic via Contrastive Learning","authors":"Meng Shen;Jinhe Wu;Ke Ye;Ke Xu;Gang Xiong;Liehuang Zhu","doi":"10.1109/TIFS.2025.3560560","DOIUrl":"10.1109/TIFS.2025.3560560","url":null,"abstract":"Traffic encryption is widely used to protect communication privacy but is increasingly exploited by attackers to conceal malicious activities. Existing malicious encrypted traffic detection methods rely on large amounts of labeled samples for training, limiting their ability to quickly respond to new attacks. These methods also are vulnerable to traffic obfuscation strategies, such as injecting dummy packets. In this paper, we propose SmartDetector, a robust malicious encrypted traffic detection method via contrastive learning. We first propose a novel traffic representation named Semantic Attribute Matrix (SAM), which can effectively distinguish between malicious and benign traffic. We also design a data augmentation method to generate diverse traffic samples, which makes the detection model more robust against different traffic obfuscation strategies. We propose a malicious encrypted traffic classifier that first pre-trains a model via contrastive learning to learn deep representations from unlabeled data, then fine-tunes the model with a supervised classifier to achieve accurate detection even with only a few labeled samples. We conduct extensive experiments with five public datasets to evaluate the performance of SmartDetector. The results demonstrate that it outperforms the state-of-the-art (SOTA) methods in three typical scenarios. Specifically, in the evasion attack detection scenario, SmartDetector achieves an F1 score and AUC above 93%, with average improvements of 19.84% and 18.17% over the SOTA method, respectively.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"4228-4242"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836844","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

Unlocking Generative Priors: A New Membership Inference Framework for Diffusion Models 解锁生成先验：一种新的扩散模型隶属推理框架

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-15 DOI: 10.1109/tifs.2025.3560776

Xiaomeng Fu, Xi Wang, Qiao Li, Jin Liu, Jiao Dai, Jizhong Han, Xingyu Gao

引用次数: 0

From Static Dense to Dynamic Sparse: Vision-Radar Fusion-Based UAV Detection 从静态密集到动态稀疏：基于视觉雷达融合的无人机探测

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-14 DOI: 10.1109/tifs.2025.3560551

Yiyao Wan, Jiahuan Ji, Fuhui Zhou, Qihui Wu, Tony Q. S. Quek

引用次数: 0

TrapNet: Model Inversion Defense via Trapdoor TrapNet：通过陷阱门进行模型反转防御

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-04-14 DOI: 10.1109/tifs.2025.3560557

Wanlun Ma, Derui Wang, Yiliao Song, Minhui Xue, Sheng Wen, Zhengdao Li, Yang Xiang

引用次数: 0