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Semantic Deep Hiding for Robust Unlearnable Examples IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-07-01 Ruohan Meng, Chenyu Yi, Yi Yu, Siyuan Yang, Bingquan Shen, Alex C. Kot
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Fair and Secure 5G and Wi-Fi Coexistence Using Robust Implicit Channel Coordination IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-07-01 Siddharth Dongre, Hanif Rahbari
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S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-28 Rizhao Cai, Zitong Yu, Chenqi Kong, Haoliang Li, Changsheng Chen, Yongjian Hu, Alex C. Kot
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Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Xiaojun Jia, Yuefeng Chen, Xiaofeng Mao, Ranjie Duan, Jindong Gu, Rong Zhang, Hui Xue, Yang Liu, Xiaochun Cao
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SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Zizhen Liu, Weiyang He, Chip-Hong Chang, Jing Ye, Huawei Li, Xiaowei Li
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A Robust Privacy-Preserving Federated Learning Model Against Model Poisoning Attacks IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Abbas Yazdinejad, Ali Dehghantanha, Hadis Karimipour, Gautam Srivastava, Reza M. Parizi
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Communication-Efficient Privacy-Preserving Neural Network Inference via Arithmetic Secret Sharing IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Renwan Bi, Jinbo Xiong, Changqing Luo, Jianting Ning, Ximeng Liu, Youliang Tian, Yan Zhang
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Balanced Encoding of Near-Zero Correlation for an AES Implementation IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Seungkwang Lee, Jeong-Nyeo Kim
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Metricizing the Euclidean Space towards Desired Distance Relations in Point Clouds IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Stefan Rass, Sandra König, Shahzad Ahmad, Maksim Goman
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A Feasibility Area Approach for Early Stage Detection of Stealthy Infiltrated Cyberattacks in Power Systems IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Ahmed A. El. Elsayed, Hadi Khani, Hany E. Z. Farag
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SGD3QN: Joint Stochastic Games and Dueling Double Deep Q-networks for Defending Malware Propagation in Edge Intelligence-Enabled Internet of Things IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-27 Yizhou Shen, Carlton Shepherd, Mujeeb Ahmed, Shigen Shen, Shui Yu
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Domain-Specific Fine-Grained Access Control for Cloud-Edge Collaborative IoT IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-26 Meiyan Xiao, Qiong Huang, Wenya Chen, Chuan Lyu, Willy Susilo
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A Practical Data Trading Protocol for Sudoku Solutions IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-26 Jie Deng, Bin Wu
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Covert Routing in Heterogeneous Networks IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-26 Justin Kong, Fikadu T. Dagefu, Terrence J. Moore
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Finger Recovery Transformer: Towards Better Incomplete Fingerprint Identification IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-26 Zexi Jia, Chuanwei Huang, Zheng Wang, Hongyan Fei, Song Wu, Jufu Feng
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Privacy-preserving Cryptocurrency with Threshold Authentication and Regulation IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-26 Zhao Zhang, Chunxiang Xu, Yunxia Han
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Optimal Private Discrete Distribution Estimation with One-bit Communication IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-26 Seung-Hyun Nam, Vincent Y. F. Tan, Si-Hyeon Lee
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Efficient Audio Steganography using Generalized Audio Intrinsic Energy with Micro-Amplitude Modification Suppression IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-24 Wenkang Su, Jiangqun Ni, Xianglei Hu, Bin Li
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Exploring Bi-Level Inconsistency via Blended Images for Generalizable Face Forgery Detection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-20 Peiqi Jiang, Hongtao Xie, Lingyun Yu, Guoqing Jin, Yongdong Zhang
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Mind the Inconsistent Semantics in Positive Pairs: Semantic Aligning and Multimodal Contrastive Learning for Text-Based Pedestrian Search IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-20 Zefeng Lu, Ronghao Lin, Haifeng Hu
Aiming at retrieving pedestrian images based on a provided textual description query, Text-Based Pedestrian Search (TBPS) has gained attention due to its implications in public security tasks such as suspect tracking. Nevertheless, the modality discrepancies between textual descriptions and visual images pose a challenge in aligning semantic information between these two modalities. Moreover, the text
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Beyond Security: Achieving Fairness in Mailmen-Assisted Timed Data Delivery IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-17 Shiyu Li, Yuan Zhang, Yaqing Song, Hongbo Liu, Nan Cheng, Dahai Tao, Hongwei Li, Kan Yang
Timed data delivery is a critical service for time-sensitive applications that allows a sender to deliver data to a recipient, but only be accessible at a specific future time. This service is typically accomplished by employing a set of mailmen to complete the delivery mission. While this approach is commonly used, it is vulnerable to attacks from realistic adversaries, such as a greedy sender (who
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Secure Model Aggregation Against Poisoning Attacks for Cross-Silo Federated Learning With Robustness and Fairness IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-17 Yunlong Mao, Zhujing Ye, Xinyu Yuan, Sheng Zhong
Federated learning (FL) is a promising approach for participants’ collaborative learning tasks with cross-silo data. Participants benefit from FL since heterogeneous data can contribute to the generalization of the global model while keeping private data locally. However, practical issues of FL, such as security and fairness, keep emerging, impeding its further development. One of the most threatening
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Multi-Faceted Knowledge-Driven Graph Neural Network for Iris Segmentation IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-12 Jianze Wei, Yunlong Wang, Xingyu Gao, Ran He, Zhenan Sun
Accurate iris segmentation, especially around the iris inner and outer boundaries, is still a formidable challenge. Pixels within these areas are difficult to semantically distinguish since they have similar visual characteristics and close spatial positions. To tackle this problem, the paper proposes an iris segmentation graph neural network (ISeGraph) for accurate segmentation. ISeGraph regards individual
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Exploring Fine-Grained Representation and Recomposition for Cloth-Changing Person Re-Identification IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-14 Qizao Wang, Xuelin Qian, Bin Li, Xiangyang Xue, Yanwei Fu
Cloth-changing person Re-IDentification (Re-ID) is a particularly challenging task, suffering from two limitations of inferior discriminative features and limited training samples. Existing methods mainly leverage auxiliary information to facilitate identity-relevant feature learning, including soft-biometrics features of shapes or gaits, and additional labels of clothing. However, this information
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MTDroid: A Moving Target Defense-Based Android Malware Detector Against Evasion Attacks IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-13 Yuyang Zhou, Guang Cheng, Shui Yu, Zongyao Chen, Yujia Hu
Machine learning (ML) has been widely adopted for Android malware detection to deal with serious threats brought by explosive malware attacks. However, it has been recently proven that ML-based detection systems exhibit inherent vulnerabilities to evasion attacks, which inject adversarial perturbations into a malicious app to hide its malicious behaviors and evade detection. To date, researchers have
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Multi-Antenna Signal Masking and Round-Trip Transmission for Privacy-Preserving Wireless Sensing IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-13 Yuwei Wang, Li Sun, Qinghe Du
Due to the openness of wireless medium and the public structure of pilot signals, wireless sensing procedure is vulnerable to eavesdropping, which causes privacy concerns. In this paper, a novel physical layer obfuscation solution termed as multi-antenna signal masking is proposed to realize privacy-preserving sensing. The privacy protection is realized via controlling the phase difference between
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A Pruned Pendant Vertex Based Index for Shortest Distance Query Under Structured Encrypted Graph IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-13 Mengdi Hu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng
The shortest distance query is used to determine the shortest distance between two vertices. Various graph encryption schemes have been proposed to achieve accurate, efficient and secure shortest distance queries for encrypted graphs. However, the majority of these schemes are inefficient or lack scalability due to the time-consuming index construction and large index storage. Moreover, none of them
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AutoSMC: An Automated Machine Learning Framework for Signal Modulation Classification IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-13 Yiran Wang, Jing Bai, Zhu Xiao, Zheng Chen, Yong Xiong, Hongbo Jiang, Licheng Jiao
The electromagnetic environments have become more complex with the development of wireless communication technology. Signal modulation classification has attracted extensive attention due to its application in electronic countermeasures and physical layer security threat prevention under complex electromagnetic environments. Excellent classification performance requirements challenge the adaptability
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Secure Similarity Queries Over Vertically Distributed Data via TEE-Enhanced Cloud Computing IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-12 Yandong Zheng, Hui Zhu, Rongxing Lu, Songnian Zhang, Yunguo Guan, Fengwei Wang, Jun Shao, Hui Li
Outsourcing big data to cloud servers has gained prominence, and growing concerns about privacy, alongside privacy-related regulations, underscore the need to encrypt data before sending them to the cloud. Nevertheless, encryption significantly hampers the query capabilities of data, particularly in the case of vertically distributed data. This paper focuses on developing secure and efficient similarity
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Hardware Secure Module Based Lightweight Conditional Privacy-Preserving Authentication for VANETs IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-11 Zihou Zhang, Jiangtao Li, Yufeng Li, Chenhong Cao, Zhenfu Cao
The security and privacy challenges faced by Vehicular Ad hoc Networks (VANETs) have led to the development of conditional privacy-preserving authentication (CPPA) schemes. Hardware security modules (HSMs) are seen as a promising solution for implementing these schemes while minimizing the burden on certificate storage. However, existing HSM-based CPPA schemes still have high computation overhead and
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Backdoor Attack With Sparse and Invisible Trigger IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-10 Yinghua Gao, Yiming Li, Xueluan Gong, Zhifeng Li, Shu-Tao Xia, Qian Wang
Deep neural networks (DNNs) are vulnerable to backdoor attacks, where the adversary manipulates a small portion of training data such that the victim model predicts normally on the benign samples but classifies the triggered samples as the target class. The backdoor attack is an emerging yet threatening training-phase threat, leading to serious risks in DNN-based applications. In this paper, we revisit
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Boosting the Transferability of Adversarial Attacks With Frequency-Aware Perturbation IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-10 Yajie Wang, Yi Wu, Shangbo Wu, Ximeng Liu, Wanlei Zhou, Liehuang Zhu, Chuan Zhang
Deep neural networks (DNNs) are vulnerable to adversarial examples, with transfer attacks in black-box scenarios posing a severe real-world threat. Adversarial perturbation is often globally manipulated image disturbances crafted in the spatial domain, leading to perceptible noise due to overfitting to the source model. Both the human visual system (HVS) and DNNs (endeavoring to mimic HVS behavior)
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Condo: Enhancing Container Isolation Through Kernel Permission Data Protection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-10 Shouyin Xu, Yuewu Wang, Lingguang Lei, Kun Sun, Jiwu Jing, Siyuan Ma, Jie Wang, Heqing Huang
Container technology is widely adopted due to its features such as light weight and ease of rapid deployment. However, as an OS-level virtualization mechanism, container isolation relies on the kernel’s security mechanisms and the kernel permission data (usually non-control flow data) used by these mechanisms. None of the existing mitigation schemes for non-control flow data attacks provide an effective
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Lawful Remote Forensics Mechanism With Admissibility of Evidence in Stochastic and Unpredictable Transnational Crime IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-04 Chit-Jie Chew, Wei-Bin Lee, Tzu-Li Sung, Ying-Chin Chen, Shiuh-Jeng Wang, Jung-San Lee
Traditional industries rapidly transcend the time and place restrictions of the country according to the technology growth by leaps and bounds over the year. Regrettably, international cybercrime incidents simultaneously explode by 2,400 million from 2020 to 2021. Undoubtedly, the real-time incident response has become the primary subject of incident handling. In this article, we aim to propose lawful
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An Anti-Disguise Authentication System Using the First Impression of Avatar in Metaverse IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-06 Zhenyong Zhang, Kedi Yang, Youliang Tian, Jianfeng Ma
Metaverse is a vast virtual world parallel to the physical world, where the user acts as an avatar to enjoy various services that break through the temporal and spatial limitations of the physical world. Metaverse allows users to create arbitrary digital appearances as their own avatars by which an adversary may disguise his/her avatar to fraud others. In this paper, we propose an anti-disguise authentication
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Nebula: Self-Attention for Dynamic Malware Analysis IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-06 Dmitrijs Trizna, Luca Demetrio, Battista Biggio, Fabio Roli
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment and logging their actions. Previous work has proposed training machine learning models, i.e., convolutional and long short-term memory networks, on homogeneous input features like runtime APIs to either detect or classify malware, neglecting other relevant information coming from heterogeneous data
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Ranker: Early Ransomware Detection Through Kernel-Level Behavioral Analysis IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-06 Huan Zhang, Lixin Zhao, Aimin Yu, Lijun Cai, Dan Meng
Ransomware is a rapidly evolving type of malware crafted to encrypt user files, rendering them inaccessible and demanding a ransom. The impact of ransomware attacks on both enterprises and individuals is significant. However, early detection of such malware remains a formidable challenge with current detection methods. In this paper, we propose Ranker, a real-time approach designed for early ransomware
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Quantum Keyless Private Communication With Decoy States for Space Channels IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-05 Ángeles Vázquez-Castro, Andreas Winter, Hugo Zbinden
With the increasing demand for secure communication in optical space networks, it is essential to develop physical-layer scalable security solutions. In this context, we present the asymptotic security analysis of a keyless quantum private communication protocol that transmits classical information over quantum states. Different from the previous literature, our protocol sends dummy (decoy) states
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PkT-SIN: A Secure Communication Protocol for Space Information Networks With Periodic k-Time Anonymous Authentication IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-04 Yang Yang, Wenyi Xue, Jianfei Sun, Guomin Yang, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng
Space Information Network (SIN) enables universal Internet connectivity for any object, even in remote and extreme environments where deploying a cellular network is difficult. Access authentication is crucial for ensuring user access control in SIN and preventing unauthorized entities from gaining access to network services. However, due to the complex communication environment in SIN, including exposed
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MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-03 Davide Alessandro Coccomini, Giorgos Kordopatis Zilos, Giuseppe Amato, Roberto Caldelli, Fabrizio Falchi, Symeon Papadopoulos, Claudio Gennaro
In this paper, we present MINTIME, a video deepfake detection method that effectively captures spatial and temporal inconsistencies in videos that depict multiple individuals and varying face sizes. Unlike previous approaches that either employ simplistic a-posteriori aggregation schemes, i.e., averaging or max operations, or only focus on the largest face in the video, our proposed method learns to
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Self-Supervised Recovery and Guide for Low-Resolution Person Re-Identification IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-03 Ke Han, Yan Huang, Liang Wang, Zikun Liu
Low-resolution person re-identification is a challenging task to match low-resolution (LR) probes with high-resolution (HR) gallery images. To address the resolution gap, existing methods typically recover missing details for LR probes by super-resolution, and then match the recovered HR images (instead of the original LR probes) with gallery images. However, they usually pre-specify fixed scale factors
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eGrass: An Encrypted Attributed Subgraph Matching System With Malicious Security IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-03 Songlei Wang, Yifeng Zheng, Xiaohua Jia, Cong Wang
It is increasingly common for enterprises/ organizations to outsource graph analytics services to the cloud. For example, enterprises may leverage the cloud to store and query large attributed graphs. Among others, subgraph matching over a large attributed graph is a common and fundamental query functionality for graph analytics. It aims to retrieve all isomorphic subgraphs for a small query graph
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A Steganography Immunoprocessing Framework Against CNN-Based and Handcrafted Steganalysis IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-03 Yijing Chen, Hongxia Wang, Wanjie Li, Wenshan Li
Performing post-processing on the stego image has promise for improving the steganography security. Nevertheless, the existing post-processing schemes neglect the characteristics of the stego image, which lack strong theoretical interpretability. Moreover, existing schemes do not fully consider the holistic steganography security against both CNN-based and handcrafted steganalyzers. In this paper,
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Enhancing Sparse Mobile CrowdSensing With Manifold Optimization and Differential Privacy IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-03 Chengxin Li, Saiqin Long, Haolin Liu, Youngjune Choi, Hiroo Sekiya, Zhetao Li
Sparse Mobile CrowdSensing (SMCS) effectively lowers sensing costs while maintaining data quality, offering an alternative approach to data collection. Unfortunately, the fact that data contain sensitive information raises serious privacy concerns. Local Differential Privacy (LDP) has emerged as the de facto standard for ensuring data privacy. However, the LDP based on the perturbation concept causes
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EGST: An Efficient Solution for Human Gaits Recognition Using Neuromorphic Vision Sensor IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-06-03 Liaogehao Chen, Zhenjun Zhang, Yang Xiao, Yaonan Wang
Traditional cameras struggle to perform in challenging scenarios such as low latency, high speed and high dynamic range. In contrast, neuromorphic vision sensors (event cameras) have great potential for robotics and computer vision due to the advantages of high temporal resolution, high dynamic range, and ultra-low resource consumption. Event cameras are novel bio-inspired sensors that monitor the
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HoneyJudge: A PLC Honeypot Identification Framework Based on Device Memory Testing IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-30 Hengye Zhu, Mengxiang Liu, Binbin Chen, Xin Che, Peng Cheng, Ruilong Deng
The widespread use of programmable logic controllers (PLCs) in critical infrastructures has given rise to escalating cybersecurity concerns regarding PLC attacks. As a proactive defense mechanism, PLC honeypots emulate genuine controllers to engage adversaries so as to observe their attack tactics and techniques. As part of the arms race between the offense and defense, multiple PLC honeypot identification
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SingleADV: Single-Class Target-Specific Attack Against Interpretable Deep Learning Systems IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-30 Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal, Hyoungshick Kim, Tamer Abuhmed
Establishing trust and helping experts debug and understand the inner workings of deep learning models, interpretation methods are increasingly coupled with these models, building interpretable deep learning systems. However, adversarial attacks pose a significant threat to public trust by making interpretations of deep learning models confusing and difficult to understand. In this paper, we present
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SIa-CBc: Sensitive Intent-Assisted and Crucial Behavior-Cognized Malware Detection Based on Human Brain Cognitive Theory IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-30 Chao Jing, Chaoyuan Cui, Yun Wu
API call sequence-based approaches are proven to have significant superiority in malware detection but generally overlook or evade two core issues: ( $i$ ) ignoring parameters and return values that contain more fine-grained security semantic sensitive information (SSSI) and ( $ii$ ) handling lengthy API call sequences roughly, causing the poor interpretability and incompleteness of program behavior
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A Secure and Efficient Blockchain Sharding Scheme via Hybrid Consensus and Dynamic Management IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-27 Meiqi Li, Xinyi Luo, Kaiping Xue, Yingjie Xue, Wentuo Sun, Jian Li
Sharding significantly enhances blockchain scalability by dividing the entire network into smaller shards that reach consensus and process transactions in parallel. Nevertheless, two new issues emerge with the adoption of sharding. One issue involves the shrinking size of consensus groups, which leads to vulnerability in consensus. Most existing works introduce periodic shuffle mechanisms to mitigate
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Shuffle Private Decentralized Convex Optimization IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-24 Lingjie Zhang, Hai Zhang
In this paper, we consider the distributed local stochastic gradient descent (SGD) algorithm by parallelizing multiple devices in the setting of stochastic convex optimization (SCO). The losses in the majority of the earlier literatures are required to satisfy Lipschitzness and smoothness, and the privacy leakage may exist in the calculation of gradients. Hence, by incorporating the Hölder smooth loss
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On the Privacy of Adaptive Cuckoo Filters: Analysis and Protection IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-23 Pedro Reviriego, Jim Apple, David Larrabeiti, Shanshan Liu, Fabrizio Lombardi
As probabilistic data structures are widely adopted in computing systems, their privacy is a major issue. Recent works have shown that even though the values stored in these structures look random, information can be extracted from them in some settings. In this paper, we consider the privacy of adaptive cuckoo filters, a probabilistic data structure that implements approximate membership checking
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Improving Transferability of adversarial samples via Critical Region-oriented Feature-level Attack IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-23 Zhiwei Li, Min Ren, Fangling Jiang, Qi Li, Zhenan Sun
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RFTrack: Stealthy Location Inference and Tracking Attack on Wi-Fi Devices IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-23 Ronghua Li, Haibo Hu, Qingqing Ye
We present RFTrack, a new indoor location inference attack on Wi-Fi devices. This attack differs from existing Wi-Fi localization methods as it does not need bulky appliance deployment or inner physical access to the place of interest. RFTrack distinguishes itself by leveraging the temporal sequence of unlabeled Received Signal Strength Indicator (RSSI) values to deduce location labels. To achieve
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Toward Stealthy Backdoor Attacks Against Speech Recognition via Elements of Sound IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-23 Hanbo Cai, Pengcheng Zhang, Hai Dong, Yan Xiao, Stefanos Koffas, Yiming Li
Deep neural networks (DNNs) have been widely and successfully adopted and deployed in various applications of speech recognition. Recently, a few works revealed that these models are vulnerable to backdoor attacks, where the adversaries can implant malicious prediction behaviors into victim models by poisoning their training process. In this paper, we revisit poison-only backdoor attacks against speech
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CMXsafe: A Proxy Layer for Securing Internet-of-Things Communications IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-22 Jorge David de Hoz Diego, Taous Madi, Charalambos Konstantinou
Security in Internet-of-Things (IoT) environments has become a major concern. This is partly due to a large number of remotely exploitable IoT vulnerabilities in service authentication and access control combined with the lack of timely technical support. To reduce the threat surface of remote vulnerability exploitation, we propose CMXsafe, a secure-by-design application-agnostic proxy layer that can
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Lattice-Aided Extraction of Spread-Spectrum Hidden Data IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-20 Fan Yang, Hao Cheng, Shanxiang Lyu, Jinming Wen, Hao Chen
This paper delves into the challenges of spread spectrum (SS) watermarking extraction, considering both reference-free and referential extraction scenarios, within the framework of lattice decoding. The orthogonality of carriers plays a crucial role in the accuracy of extraction, impacting the bit error rate (BER). When carriers lack sufficient orthogonality, conventional reference-free extraction
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Libras: A Fair, Secure, Verifiable, and Scalable Outsourcing Computation Scheme Based on Blockchain IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-20 Lijuan Huo, Libing Wu, Zhuangzhuang Zhang, Chunshuo Li, Debiao He, Jing Wang
Existing multitask outsourcing computations struggle to guarantee the fairness for participants and the correctness of the computation results. Some solutions use blockchain to address the fairness issue in outsourcing computations. However, blockchain suffers from poor data privacy due to its public and transparent nature, as well as the latency because of limited scalability. To effectively confront
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Generic Construction of Conditional Privacy-Preserving Certificateless Signatures With Efficient Instantiations for VANETs IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-20 Lang Pu, Chao Lin, Jingjing Gu, Xinyi Huang, Debiao He
Vehicular Ad-hoc Networks (VANETs) constitute crucial elements within intelligent transportation systems. However, the rapid development of VANETs has brought forth an increasing number of security concerns. Conditional Privacy-Preserving Certificateless Signature (CPP-CLS) has emerged as a promising solution to ensure data security, preserve vehicle anonymity, and establish unlinkability in VANETs
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Dedicated Quantum Attacks on XOR-Type Function With Applications to Beyond-Birthday- Bound MACs IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-20 Tairong Shi, Wenling Wu, Bin Hu, Jie Guan, Han Sui, Senpeng Wang, Mengyuan Zhang
A lot of work in the field of quantum cryptanalysis is currently devoted to finding applications of Grover-meets-Simon algorithm and its complexity is given in the form of $\mathcal {O}$ , but research on how to implement the attack efficiently is still insufficient. After all, it is crucial to study quantum attacks in resource-limited situations, according to NIST’s guidance on circuit depth. This
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Pilot Spoofing Attack on the Downlink of Cell-Free Massive MIMO: From the Perspective of Adversaries IEEE Trans. Inform. Forensics Secur. (IF 6.3) Pub Date : 2024-05-20 Weiyang Xu, Ruiguang Wang, Yuan Zhang, Hien Quoc Ngo, Wei Xiang
The channel hardening effect is less pronounced in the cell-free massive multiple-input multiple-output (mMIMO) system compared to its cellular counterpart, making it necessary to estimate the downlink effective channel gains to ensure decent performance. However, the downlink training inadvertently creates an opportunity for adversarial nodes to launch pilot spoofing attacks (PSAs). First, we demonstrate