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TRE-DSP: A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and Telecommunications College of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Department of Computer Science and Engineering University College of Engineering Department of Computer Engineering National Institute of Technology Kurukshetra

With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Global Spatial-Temporal information Encoder-Decoder Based Action Segmentation in Untrimmed Video

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Tsinghua Science and Technology 2025年第1期30卷 290-302页

作者： Yichao Liu Yiyang Sun Zhide Chen Chen Feng Kexin Zhu Department of Computer and Cyberspace Security Fujian Normal UniversityFuzhou 350007China Department of Information Engineering Sun Yat-sen UniversityKaohsiung 80424China

Action segmentation has made significant progress,but segmenting and recognizing actions from untrimmed long videos remains a challenging *** state-of-the-art methods focus on designing models based on temporal ***,the limitations of modeling long-term temporal dependencies and the inflexibility of temporal convolutions restrict the potential of these *** address the issue of over-segmentation in existing action segmentation methods,which leads to classification errors and reduced segmentation quality,this paper proposes a global spatial-temporal information encoder-decoder based action segmentation *** method proposed in this paper uses the global temporal information captured by refinement layer to assist the Encoder-Decoder(ED)structure in judging the action segmentation point more accurately and,at the same time,suppress the excessive segmentation phenomenon caused by the ED *** method proposed in this paper achieves 93%frame accuracy on the constructed real Tai Chi action *** experimental results prove that this method can accurately and efficiently complete the long video action segmentation task.

关键词： Encoder-Decoder(ED) Bidirectional Long Short-Term Memory(BiLSTM) Tai Chi action segmentation untrimmed video

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ARP Spoofing Mitigation for the E2 Interface in Open RAN: An xApp Approach 25th

ARP Spoofing Mitigation for the E2 Interface in Open RAN:...

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25th International Conference on information security Applications, WISA 2024

作者： Kim, Jihye Park, Jaehyoung Lee, Jong-Hyouk Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9789819616237

In the 5G-Advanced and 6G era, the Open Radio Access Network (Open RAN) will support hardware and software interoperability between different vendors. The O-RAN Alliance, which leads the Open RAN initiative, has released standards addressing potential threats and mitigation requirements to the framework. While the standards specify Man-in-the-Middle (MitM) attacks on the E2 interface and recommend using IPsec, a Layer 3 security protocol, they do not cover defenses against Layer 2 attacks, such as Address Resolution Protocol (ARP) spoofing. This paper proposes an xApp with preventive and reactive mechanisms to protect the E2 interface from ARP spoofing in Open RAN. The xApp proactively prevents attacks through a static ARP table and continuously monitors the E2 interface to detect and react to the ARP spoofing, offering better network resilience than existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radio access networks

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security Function for 5G-Advanced and 6G: Malicious UE Detection in Network Slicing 25th

Security Function for 5G-Advanced and 6G: Malicious UE Det...

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25th International Conference on information security Applications, WISA 2024

作者： Park, Jaehyoung Kim, Jihye Lee, Jong-Hyouk Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul05006 Korea Republic of Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of

ISBN: (纸本)9789819616237

Starting with Software Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, network slicing is emerging as a critical technology in 5G-Advanced and 6G mobile communications to allocate logical networks end-to-end, meeting diverse service requirements. However, the advent of network slicing introduces new security threats, including inter-slice security, intra-slice security, and lifecycle security of network slicing. While recent research has focused on security orchestration technologies using SDN/NFV for network slicing and AI-based attack detection, there remains a gap in research on security by design within the components of 3GPP 5G systems. Therefore, this paper investigates security by design in the 5G-Advanced and 6G architectures. To address these issues, we propose a malicious User Equipment (UE) detection function to identify attacks within the network-slicing environment. We illustrate the interaction between the detection function and other 3GPP 5G system components for network slicing security with its algorithm for detecting malicious UEs in network slices. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Malicious UE Network Slicing security by Design

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Enhancing Federated Feature Selection through Synthetic Data and Zero Trust Integration

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2126-2140页

作者： Madathil, Nisha Thorakkattu Alrabaee, Saed Belkacem, Abdelkader Nasreddine United Arab Emirates University College of IT Department of Information Systems and Security United Arab Emirates United Arab Emirates University College of IT Information Systems and Security department United Arab Emirates UAE University Computer and Network Engineering Department Al Ain United Arab Emirates

Federated Learning (FL) allows healthcare organizations to train models using diverse datasets while maintaining patient confidentiality collaboratively. While promising, FL faces challenges in optimizing model accuracy and communication efficiency. To address these, we propose an algorithm that combines feature selection with synthetic data generation, specifically targeting medical datasets. Our method eliminates irrelevant local features, identifies globally relevant ones, and uses synthetic data to initialize model parameters, improving convergence. It also employs a zero-trust model, ensuring that data remain on local devices and only learned weights are shared with the central server, enhancing security. The algorithm improves accuracy and computational efficiency, achieving communication efficiency gains of 4 to 14 through backward elimination and threshold variation techniques. Tested on a federated diabetic dataset, the approach demonstrates significant improvements in the performance and trustworthiness of FL systems for medical applications. © 1983-2012 IEEE.

关键词： Federated learning

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Privacy Enhanced P2P Power Trading Using DID-Based Identity Verification in a Microgrid 25th

Privacy Enhanced P2P Power Trading Using DID-Based Identity ...

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25th International Conference on information security Applications, WISA 2024

作者： Lee, Taeyang Department of Computer and Information Security and Convergence Engineering for Intelligent Drone Sejong University Seoul Korea Republic of

ISBN: (纸本)9789819616237

Smart meters play a crucial role in accurately measuring power usage and distribution data in microgrids. However, fine-grained data collection raises significant privacy concerns. To address this, it is essential to develop a distributed power trading system that is both trustworthy and privacy-preserving. In this paper, we propose a peer-to-peer power trading mechanism that ensures privacy through identity verification based on decentralized identifiers, zero-knowledge proofs, and pseudonyms. Besides, we introduce an efficient claims verification scheme with Boneh-Lynn-Shacham aggregate signature. We also validate the security of our proposed mechanism through the Real-or-Random model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Analysis of Tomato Leaf Diseases Detection Models 8th

Analysis of Tomato Leaf Diseases Detection Models

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Satyarthi, Devshri Arya, K.V. Rathore, Santosh Singh Mishra, Richa Multimedia and Information Security Research Group Department of Computer Science and Engineering ABV-IIITM 474015 Gwalior India

ISBN: (纸本)9789819738588

Plant leaf disease distresses the growth of the plant. Therefore, early detection and classification of plant leaf disease is vital for healthy crops such as tomatoes. This early detection of tomato leaf disease in plants is also necessary to stop victims in the agriculture field, in tomato leaf disease plants such as bacterial, early blight, septoria, mosaic virus, and one healthy class. Recently, various deep learning-based architectures, such as AlexNet, ResNet50, and VGG16, have been widely applied for classifying and detecting leaf diseases. Primarily, image processing, convolution neural networks, and deep learning methods have been explored to develop a robust detection model to detect, identify, and classify tomato leaf diseases. However, the previous works’ comprehensive analysis of these models is missing. Therefore, this paper analyzes different deep-learning models for Tomato leaf disease detection. It includes data collection, pre-processing, feature extraction, classification, identification, and detection using AlexNet, ResNet50, and VGG16. The experimental analysis is performed on a plant village dataset for a Tomato plant consisting of one plant, four diseases, and one healthy class. The experimental results showed that ResNet yielded the best performance among the used deep learning models, producing an accuracy value of 98.15%, precision of 98.19%, recall of 97.25%, and F1-score of 97.72%. It is followed by the AlexNet model with an accuracy value of 97.8%, precision of 98.45%, recall of 95.82%, and F1-score of 96.14%. The VGG 16 had the lowest accuracy value of 75%, precision of 74.04%, recall of 68.06%, and F1-score of 70.93%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fruits

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Unveiling the Pruning Risks on Privacy Vulnerabilities of Deep Neural Networks

Unveiling the Pruning Risks on Privacy Vulnerabilities of De...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kuang, Wenxin Liang, Qizhuang Sun, Peng Fu, Wei Hu, Qiao Hu, Yupeng College of Computer Science and Electronic Engineering Hunan University Changsha China Department of Information Security Naval University of Engineering Wuhan China

ISBN: (纸本)9798350368741

Large-scale deep neural networks (DNNs), such as large language models, have gained immense popularity due to their outstanding performance across various tasks. However, their application in resource-constrained scenarios faces significant challenges due to the high computational costs and memory usage of these models during inference. Model pruning emerges as a viable technique to mitigate these limitations by reducing the computational complexity of deep models. While existing research primarily focuses on maximizing inference efficiency without compromising accuracy, the privacy implications of pruning techniques remain largely unexplored. In this work, we systematically investigate the impact of popular pruning techniques on the privacy vulnerabilities of DNNs. We begin by applying common pruning schemes to various DNNs and evaluating their privacy risks, both before and after pruning, using model inversion attacks. We then analyze how the pruning rate and granularity affect these privacy vulnerabilities. Moreover, we conduct experiments on both original and pruned models equipped with defenses to confirm that the increase in privacy risks following pruning is not merely coincidental. Finally, we offer guidelines for the careful application of pruning techniques. Our findings serve as a cautionary note, highlighting the inherent privacy risks associated with current pruning schemes and providing valuable insights for developing pruning methods that are both efficient and secure. © 2025 IEEE.

关键词： Deep neural networks Model inversion attack Network pruning Privacy vulnerabilities

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