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Fingerprint Extraction through Distortion Reconstruction (FEDR): A CNN-based Approach to RF Fingerprinting IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-24 Jose A. Gutierrez del Arroyo, Brett J. Borghetti, Michael A. Temple
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Efficient and Privacy-Preserving Encode-Based Range Query over Encrypted Cloud Data IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-23 Yanrong Liang, Jianfeng Ma, Yinbin Miao, Yuan Su, Robert H. Deng
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BASUV: A Blockchain-Enabled UAV Authentication Scheme for Internet of Vehicles IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-23 Mingyue Xie, Zheng Chang, Hongwei Li, Geyong Min
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Adversarial Perturbation Prediction for Real-Time Protection of Speech Privacy IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-23 Zhaoyang Zhang, Shen Wang, Guopu Zhu, Dechen Zhan, Jiwu Huang
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IFAST: Weakly Supervised Interpretable Face Anti-Spoofing from Single-Shot Binocular NIR Images IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-23 Jiancheng Huang, Donghao Zhou, Jianzhuang Liu, Linxiao Shi, Shifeng Chen
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Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-23 Chenhao Lin, Fangbin Yi, Hang Wang, Jingyi Deng, Zhengyu Zhao, Qian Li, Chao Shen
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Neighbor Consistency and Global-Local Interaction: A Novel Pseudo-label Refinement Approach for Unsupervised Person Re-Identification IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-20 De Cheng, Haichun Tai, Nannan Wang, Chaowei Fang, Xinbo Gao
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Boosting Adversarial Transferability via Logits Mixup with Dominant Decomposed Feature IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-20 Juanjuan Weng, Zhiming Luo, Shaozi Li, Dazhen Lin, Zhun Zhong
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Dangers behind Charging VR Devices: Hidden Side Channel Attacks via Charging Cables IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-20 Jiachun Li, Yan Meng, Yuxia Zhan, Le Zhang, Haojin Zhu
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Open Set Learning for RF-based Drone Recognition via Signal Semantics IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-20 Ningning Yu, Jiajun Wu, Chengwei Zhou, Zhiguo Shi, Jiming Chen
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Themis: Robust and Light-Client Dynamic Searchable Symmetric Encryption IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-19 Yubo Zheng, Peng Xu, Miao Wang, Wanying Xu, Wei Wang, Tianyang Chen, Hai Jin
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A Wavelet-Based Memory Autoencoder for Noncontact Fingerprint Presentation Attack Detection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-19 Yi-Peng Liu, Hangtao Yu, Haonan Fang, Zhanqing Li, Peng Chen, Ronghua Liang
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Minimizing Malware Propagation in Internet of Things Networks: An Optimal Control Using Feedback Loop Approach IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-19 Mousa Tayseer Jafar, Lu-Xing Yang, Gang Li, Qingyi Zhu, Chenquan Gan
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LiDiNet: A Lightweight Deep Invertible Network for Image-in-Image Steganography IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-18 Fengyong Li, Yang Sheng, Kui Wu, Chuan Qin, Xinpeng Zhang
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Who Stole My NFT? Investigating Web3 NFT Phishing Scams on Ethereum IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-18 Jingjing Yang, Jieli Liu, Dan Lin, Jiajing Wu, Baoying Huang, Quanzhong Li, Zibin Zheng
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FS-LLRS: Lattice-based Linkable Ring Signature with Forward Security for Cloud-assisted Electronic Medical Records IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-18 Xue Chen, Shiyuan Xu, Shang Gao, Yu Guo, Siu-Ming Yiu, Bin Xiao
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Privacy-Preserving Federated Learning with Improved Personalization and Poison Rectification of Client Models IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-18 Yihao Cao, Jianbiao Zhang, Yaru Zhao, Hong Shen, Haoxiang Huang
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Reinventing Multi-User Authentication Security from Cross-Chain Perspective IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-18 Yongyang Lv, Ruitao Feng, Maode Ma, Manqing Zhu, Hanwei Wu, Xiaohong Li
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GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-16 Yaning Zhang, Zitong Yu, Tianyi Wang, Xiaobin Huang, Linlin Shen, Zan Gao, Jianfeng Ren
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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof Data Storage IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-16 Ziyuan Yang, Ming Kang, Andrew Beng Jin Teoh, Chengrui Gao, Wen Chen, Bob Zhang, Yi Zhang
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A Novel PHY-layer Spoofing Attack Detection Scheme based on WGAN-Encoder Model IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-13 Wei Xie, Hongjun Wang, Zimo Feng, Chunlai Ma
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Scalable Universal Adversarial Watermark Defending against Facial Forgery IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-13 Tong Qiao, Bin Zhao, Ran Shi, Meng Han, Mahmoud Hassaballah, Florent Retraint, Xiangyang Luo
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What is Now Possible? Security Evaluation on Univariate DPA Attacks with Inaccurate Leakage Models IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Jiangshan Long, Changhai Ou, Chenxu Wang, Zhu Wang, Yongbin Zhou
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A Video Visual Security Metric Based on Spatiotemporal Self-Attention IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Bo Tang, Fengdong Li, Jianbo Liu, Cheng Yang
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Accountable Secret Committee Election and Anonymous Sharding Blockchain Consensus IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Mingzhe Zhai, Yizhong Liu, Qianhong Wu, Bo Qin, Haibin Zheng, Xiaopeng Dai, Zhenyang Ding, Willy Susilo
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Fusion Graph Structure Learning-based Multivariate Time Series Anomaly Detection with Structured Prior Knowledge IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Shiming He, Genxin Li, Kun Xie, Pradip Kumar Sharma
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DWare: Cost-Efficient Decentralized Storage with Adaptive Middleware IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Yuefeng Du, Anxin Zhou, Cong Wang
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Identity-Based Encryption with Disjunctive, Conjunctive and Range Keyword Search from Lattices IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Zesheng Lin, Hongbo Li, Xinjian Chen, Meiyan Xiao, Qiong Huang
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Indelible “Footprints” of Inaudible Command Injection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-12 Zhongjie Ba, Bin Gong, Yuwei Wang, Yuxuan Liu, Peng Cheng, Feng Lin, Li Lu, Kui Ren
Inaudible command injection transmits inaudible ultrasounds to inject adversarial speech commands into a voice assistant, therefore manipulating voice control systems (e.g., a garage door or a security camera) for illegitimate purposes. Although the attack is inaudible, we find it does leave visible “footprints”. Such attack “footprints” are the side product due to the interaction between the attack
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A Case-Control Study to Measure Behavioral Risks of Malware Encounters in Organizations IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-10 Marcello Meschini, Giorgio Di Tizio, Marco Balduzzi, Fabio Massacci
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An Efficient and Verifiable Encrypted Data Filtering Framework Over Large-Scale Storage in Cloud Edge IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-09 Qinlong Huang, Chao Wang, Boyu Lu
The rapid growth of edge computing is accelerating data subscriptions between cloud platforms and mobile subscribers, but sensitive information in these data faces security and privacy concerns. Fortunately, matchmaking attribute-based encryption (MABE) as a new type of encrypted data filtering mechanism has been introduced in cloud edge, which not only enforces fine-grained access control over the
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Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-06 Jiawei Zhao, Kejiang Chen, Xiaojian Yuan, Yuang Qi, Weiming Zhang, Nenghai Yu
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TIM: Enabling Large-Scale White-Box Testing on In-App Deep Learning Models IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-06 Hao Wu, Yuhang Gong, Xiaopeng Ke, Hanzhong Liang, Fengyuan Xu, Yunxin Liu, Sheng Zhong
Intelligent Applications (iApps), equipped with in-App deep learning (DL) models, are emerging to provide reliable DL inference services. However, in-App DL models are typically compiled into inference-only versions to enhance system performance, thereby impeding the evaluation of DL models. Specifically, the assessment of in-App models currently relies on black-box testing methods rather than direct
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A Novel Evaluation Framework for Biometric Security: Assessing Guessing Difficulty as a Metric IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-06 Tim Van hamme, Giuseppe Garofalo, Enrique Argones Rúa, Davy Preuveneers, Wouter Joosen
Biometric authentication systems have traditionally relied on the False Match Rate (FMR) to evaluate security against impersonation threats. However, this metric alone is insufficient for assessing vulnerabilities to statistical attacks because it cannot account for the non-uniformity of mismatches and atypical inputs that adversaries may manipulate. To address this issue, we propose a new evaluation
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Collusion-Resilient Privacy-Preserving Database Fingerprinting IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-06 Shunsheng Zhang, Youwen Zhu, Ao Zeng
Database sharing may bring about privacy disclosure and illegal redistribution. A previously proposed entry-level Differential Privacy FingerPrinting mechanism (DPFP) for relational database achieves privacy and liability guarantees simultaneously. However, it is only robust against common attacks from a vicious Data Analyzer (DA) and lacks robustness against logical AND or OR collusion attack even
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Chaos-based Index-of-Min Hashing Scheme for Cancellable Biometrics Security IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-05 Wanying Dai, Beibei Li, Qingyun Du, Ziqing Zhu, Ao Liu
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ABSyn: An Accurate Differentially Private Data Synthesis Scheme With Adaptive Selection and Batch Processes IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Jingyu Jia, Xinhao Li, Tong Li, Zhewei Liu, Chang Tan, Siyi Lv, Liang Guo, Changyu Dong, Zheli Liu
In private data publishing, a promising solution is generating synthetic data that enables any query on the private dataset while satisfying differential privacy. Over the past decade, researchers mainly focused on improving the query accuracy of synthetic data. However, the limitations of existing works restrict them from achieving a better trade-off between accuracy and privacy. In this paper, we
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Querying Twice to Achieve Information-Theoretic Verifiability in Private Information Retrieval IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Stanislav Kruglik, Son Hoang Dau, Han Mao Kiah, Huaxiong Wang, Liang Feng Zhang
Private Information Retrieval (PIR) protocols allow a client to retrieve any file of interest while keeping the files identity hidden from the database servers. While many existing PIR protocols assume servers to be honest but curious, we investigate the scenario of dishonest servers that provide incorrect answers to mislead clients into obtaining wrong results. We propose a unified framework for polynomial
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Toward Generating Communication Graph Datasets for Botnet Detection in Autonomous Systems IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Yuhao Yan, Bo Lang, Xiaoyuan Meng, Nan Xiao
Botnet is one of the main threats to cybersecurity because of its concealment and hazardous nature, especially in autonomous systems (ASs), such as campus networks. Graph-based detection methods are attracting increasing attention due to their ability to find and use the topological features of botnets. However, constructing or obtaining a botnet dataset is always difficult, and almost all existing
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Defending Against Membership Inference Attack for Counterfactual Federated Recommendation With Differentially Private Representation Learning IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Xiuwen Liu, Yanjiao Chen, Shanchen Pang
When it comes to the marriage of federated learning and personalized recommendation services (FedRec), characterizing user-item interaction behaviors is a long-standing and unresolved issue, highlighting the growing data privacy concerns due to the inherent openness of recommender systems. As the new interaction-level membership inference attacks on FedRecs have recently surfaced, quite possibly such
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Adversarial Examples Against WiFi Fingerprint-Based Localization in the Physical World IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Jiakai Wang, Ye Tao, Yichi Zhang, Wanting Liu, Yusheng Kong, Shaolin Tan, Rongen Yan, Xianglong Liu
WiFi Fingerprint-based Localization (WFL) has recently achieved promising results in the bloom of deep learning techniques. Unfortunately, current studies reveal the great risks of deep-learning models when facing adversarial attacks, raising broader concerns about Deep-learning-based WiFi Fingerprint Localization Models (DFLMs). However, real-world adversarial attacks targeting DFLMs are not fully
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Functionality-Verification Attack Framework Based on Reinforcement Learning Against Static Malware Detectors IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Buwei Tian, Junyong Jiang, Zichen He, Xin Yuan, Lu Dong, Changyin Sun
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Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-09-02 Xinjian Luo, Yangfan Jiang, Fei Wei, Yuncheng Wu, Xiaokui Xiao, Beng Chin Ooi
Diffusion models have recently gained significant attention in both academia and industry due to their impressive generative performance in terms of both sampling quality and distribution coverage. Accordingly, proposals are made for sharing pre-trained diffusion models across different organizations, as a way of improving data utilization while enhancing privacy protection by avoiding sharing private
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iBA: Backdoor Attack on 3D Point Cloud via Reconstructing Itself IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-30 Yuhao Bian, Shengjing Tian, Xiuping Liu
The widespread deployment of deep neural networks (DNNs) for 3D point cloud processing contrasts sharply with their vulnerability to security breaches, particularly backdoor attacks. Studying these attacks is crucial for raising security awareness and mitigating potential risks. However, the irregularity of 3D data and the heterogeneity of 3D DNNs pose unique challenges. Existing methods frequently
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Combing Multiple Visual Stimuli to Enhance the Performance of VEP-Based Biometrics IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-30 Haomin Qu, Hongze Zhao, Qingguo Wei, Weihua Pei, Xiaorong Gao, Yijun Wang
In recent years, electroencephalography (EEG) has received increasing attention in the field of biometrics because of its unique advantages such as covertness, resistance to spoofing, sensitivity to emotional and mental states, and continuous nature. Visual evoked potentials (VEPs) have been widely used in EEG-based biometrics owing to fast recognition speed and high accuracy. This study proposes a
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Identity-Based Group Encryption With Keyword Search Against Keyword Guessing Attack IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-30 Wei Wang, Dongli Liu, Zilin Zheng, Peng Xu, Laurence Tianruo Yang
Public key Encryption with Keyword Search (PEKS) has emerged as a solution for the receiver to securely search the sender’s encrypted data on the cloud. However, the PEKS scheme is threatened by the Keyword Guessing Attack (KGA), which leaks the receiver’s keyword privacy. To resist KGA, researchers have inherited the authentication mechanism into the PEKS system (PAEKS) but also forbid using one trapdoor
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Dynamic Adaptation RFF Identification Method Leveraging Cognitive Representation Learning IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-29 Yang Peng, Pengfei Liu, Qianyun Zhang, Lantu Guo, Yuchao Liu, Yu Wang, Yun Lin, Guan Gui
The evolution of wireless communication technologies has brought significant conveniences but also raised security concerns. Radio frequency fingerprint (RFF) is a potential feature, which can uniquely identify a specific emitter. The integration of Deep Learning (DL) has further enhanced the reliability of RFF identification. However, DL methods often struggle in dynamic communication environments
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SCAFinder: Formal Verification of Cache Fine-Grained Features for Side Channel Detection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-29 Shixuan Zhang, Haixia Wang, Pengfei Qiu, Yongqiang Lyu, Hongpeng Wang, Dongsheng Wang
Recent research has unveiled numerous cache-timing side-channel attacks exploiting the side effects of fine-grained cache features, such as coherence protocol and prefetch, among others. Traditional modeling methods and verification techniques are insufficient for verifying caches with fine-grained features and detecting cache timing vulnerabilities. There is a necessity for comprehensive verification
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Generative Imperceptible Attack With Feature Learning Bias Reduction and Multi-Scale Variance Regularization IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-29 Weicheng Xie, Zenghao Niu, Qinliang Lin, Siyang Song, Linlin Shen
Existing studies have shown that malicious and imperceptible adversarial samples may significantly weaken the reliability and validity of deep learning systems. Since gradient-based attack algorithms may result in higher generation latency or demand large computation overhead, generative attack methods are frequently considered. However, the effectiveness and imperceptibility are still the main concerns
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Device-Side Lightweight Mutual Authentication and Key Agreement Scheme Based on Chameleon Hashing for Industrial Internet of Things IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Qingyang Zhang, Xiaolong Zhou, Hong Zhong, Jie Cui, Jiaxin Li, Debiao He
Several authentication and key agreement (AKA) schemes have been proposed to ensure secure communication in the Industrial Internet of Things (IIoT). However, most of these schemes face two primary problems. First, they cannot resist various attacks, such as impersonation and device capture attacks. Second, these schemes overlook the resource-constrained IIoT devices, failing to guarantee lightweight
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PowerGuard: Using Power Side-Channel Signals to Secure Motion Controllers in ICS IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Shijie Li, Yuqi Chen, Xin Chen, Zedong Li, Dongliang Fang, Kaixiang Liu, Shichao Lv, Limin Sun
Motion control systems, extensively utilized in domains like 3D printing, CNC machining, and robotic arm operations, are pivotal in modern manufacturing and automation processes. Consequently, a specific category of attacks, designed to target these systems, can manipulate the movements of controlled objects while replaying false sensor readings to evade existing tools, thereby severely disrupting
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Robust and Accurate Hand Gesture Authentication with Cross-Modality Local-Global Behavior Analysis IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Yufeng Zhang, Wenxiong Kang, Wenwei Song
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Proof of Finalization: A Self-Fulfilling Function of Blockchain IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Aixian Deng, Qian Ren, Yingjun Wu, Hong Lei, Bangdao Chen
Blockchain has been widely used in various industries for providing trustworthy data. On-chain data can be regarded as trusted after it is finalized by blockchain consensus, namely after the data is believed to be immutable. Unfortunately, nodes with poor/isolated network conditions are still susceptible to data spoofing attacks of blockchain view, spawning kinds of severe attacks. For example, a light
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FedPTA: Prior-Based Tensor Approximation for Detecting Malicious Clients in Federated Learning IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Xutong Mu, Ke Cheng, Teng Liu, Tao Zhang, Xueli Geng, Yulong Shen
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Using Graph Neural Networks to Improve Generalization Capability of the Models for Deepfake Detection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Huimin She, Yongjian Hu, Beibei Liu, Jicheng Li, Chang-Tsun Li
Deepfake detection plays a key role in preventing the misuses of artificial intelligence in video editing. Current deep learning-based deepfake detection methods often perform quite well in intra-dataset testing, but they may lose good performance in cross-dataset testing. In other words, generalization capability is still a crucial problem to be resolved. In this paper, we address deepfake detection
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Robust Multi-Factor Authentication for WSNs with Dynamic Password Recovery IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Liufu Zhu, Ding Wang
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A Deep Reinforcement Learning-Based Deception Asset Selection Algorithm in Differential Games IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Weizhen He, Jinglei Tan, Yunfei Guo, Ke Shang, Hengwei Zhang
Currently, there are various problems in the field of network attack-defense analysis and deception asset deployment of game theory-based, such as difficulties in constructing attack and defense models and determining real-time attack and defense strategies. To address these problems, this study proposes a differential game deception asset selection algorithm based on multi-agent deep reinforcement
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SAMCU: Secure and Anonymous Multi-Channel Updates in Payment-Channel Networks IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-28 Jianhuan Wang, Shang Gao, Guyue Li, Keke Gai, Bin Xiao
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ACE-WARP: A Cost-Effective Approach to Proactive and Non-Disruptive Incident Response in Kubernetes Clusters IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-26 Sima Bagheri, Hugo Kermabon-Bobinnec, Mohammad Ekramul Kabir, Suryadipta Majumdar, Lingyu Wang, Yosr Jarraya, Boubakr Nour, Makan Pourzandi
A large-scale cluster of containers managed with an orchestrator like Kubernetes are behind many cloud-native applications today. However, the weaker isolation provided by containers means attackers can potentially exploit a vulnerable container and then escape its isolation to cause more severe damages to the underlying infrastructure and its hosted applications. Defending against such an attack using
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Verifiable Coded Computation of Multiple Functions IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-08-26 Wilton Kim, Stanislav Kruglik, Han Mao Kiah
We consider the problem of evaluating distinct multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its corresponding dataset and propose a generalization of the Lagrange Coded Computing framework (Yu et al., 2019) to perform all computations