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Ural Symposium on Biomedical engineering, Radioelectronics and information Technology (USBEREIT)

作者： Pavel V. Mizinov Natalia S. Konnova Oleg V. Kravchenko Dobroslav P. Egorov Gavriil V. Osipenko Department of information security Bauman Moscow State Technical University Moscow Russian Federation Federal Research Center Computer Science and Control of RAS Moscow Russian Federation Engineering and Electronics of RAS Kotel’nikov Institute of Radio Moscow Russian Federation Moscow Aviation Institute Moscow Russian Federation

ISBN: (数字)9798350362893

ISBN: (纸本)9798350362909

Biometric identification methods using vascular modalities (vein pattern of the finger, wrist, ventral and dorsal part of the metacarpal) use near infrared (NIR) illumination to obtain an image of the vein pattern. The “classic” attack scenario for this type of biometric system involves the use of a photograph of the user’s vein pattern, printed on ordinary office paper and obtained under NIR illumination, as a presentation attack tool. This attack scenario has been well studied by researchers and there are several reliable ways to detect such an attack. New biometric attack vectors on these systems involve the use of attack tools created using NIR reflective compounds. In order to develop effective methods to detect and counter this type of attack, it is necessary to study the characteristics of the attack tool in each of the $750-950 \mathrm{~nm}$ NIR sub-bands of the venous channel imaging band. In this paper, the authors present the stages of creating a hardware tool for obtaining an image of the vascular pattern to solve the problem at hand.

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A Fast Authentication Scheme for Cross-Network-Slicing Based on Multiple Operators in 5G Environments 1

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8th International Symposium on security in Computing and Communications, SSCC 2020

作者： Huang, Jheng-Jia Fan, Chun-I Hsu, Yu-Chen Karati, Arijit Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan Intelligent Electronic Commerce Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

With 5G Network Slicing, the 5G telecommunication operators can achieve the goal of supporting users with a variety of different services and can also create a slice with certain unique characteristics. For example: Enhanced Mobile Broadband slicing, Ultra-reliable and Low Latency Communications slicing, etc. However, the traditional authentication mechanism does not address any concrete strategy for network slicing handover in 5G, so that the computational process must be still calculated by the core network. Hence, we propose a network slicing handover authentication scheme that not only satisfies the standards defined by 3rd Generation Partnership Project but also achieves low time latency through delegating computation overhead to the edge clouds. In addition, we incorporate the concepts of the proxy re-signature and certificateless signature in the scheme. As a result, when users need to use the network slicing services across the telecommunications operators, they can still meet the requirements of reducing the time latency in the process of the authentication flows. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Optimization Technique for Deep Learning Methodology on Power Side Channel Attacks

Optimization Technique for Deep Learning Methodology on Powe...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Amjed Abbas Ahmed Mohammad Kamrul Hasan Nazmus Shaker Nafi Azana Hafizah Aman Shayla Islam Mohammad Siab Nahi Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq GS-APAC Kellogg Brown and Root Melbourne Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

The first non-profiled side-channel attack (SCA) method using deep learning is Timon's Differential Deep Learning Analysis (DDLA). This method is effective in retrieving the secret key with the help of deep learning metrics. The Neural Network (NN) has to be trained numerous times since the proposed approach increases the learning cost with the key sizes, making it hard to assess the results from the intermediate stage. In this research, we provide three possible answers to the issues raised above, along with any challenges that could result from trying to solve these issues. We will start by offering an updated algorithm that has been modified to be able to keep track of the metrics during the intermediary stage. Next, we provide a parallel NN structure and training technique for a single network. This saves a lot of time by eliminating the need to repeatedly retrain the same model. The newly designed algorithm significantly sped up attacks when compared to the previous one. Thus, we propose employing shared layers to overcome memory challenges in parallel structure and improve performance. We evaluated our approaches by presenting non-profiled attacks on ASCAD dataset and a Chip Whisperer-Lite power usage dataset. Power utilisation was studied using both datasets. The shared layers strategy we created was up to 134 times more successful than the prior technique when used to the ASCAD database.

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Matching Game for Optimized Association in Quantum Communication Networks

Matching Game for Optimized Association in Quantum Communica...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Mahdi Chehimi Bernd Simon Walid Saad Anja Klein Don Towsley Mérouane Debbah Bradley Department of Electrical and Computer Engineering Wireless@VT Virginia Tech Arlington VA USA Communication Engineering Lab Technische Universität Darmstadt Darmstadt Germany Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon College of Information and Computer Sciences University of Massachusetts Amherst Amherst MA USA Electrical Engineering and Computer Science Department Khalifa University of Science and Technology Abu Dhabi UAE

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

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SLA-SCA: Secure and Lightweight Authentication and Key Agreement Mechanism for Smart City Applications

SLA-SCA: Secure and Lightweight Authentication and Key Agree...

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Communications, Computing, Cybersecurity, and Informatics (CCCI), International Conference on

作者： Bhairvi Pant Mohammad S. Obaidat Shreya Singh Mudita Pant Mohammad Wazid Ashok Kumar Das Kuei-Fang Hsiao Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China School of Computing SRM University Kattankulathur TN India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Computer Science Department University of Texas-Permian Basin Odessa USA

The notion of a smart city is a modern concept characterized by the widespread integration of the Internet of Things (IoT) across various facets of urban life, including healthcare, traffic management, agriculture, and more. This establishes real-time communication and data exchange between all users, servers and devices through the Internet. As we begin to discuss communication between parties, one must also recognize the imperative need for this exchange to be secure and private, as such exchanges are extremely vulnerable to attacks such as man-in-the-middle and replay attacks and even situations like compromising of anonymity during exchanges. This paper focuses on finding an innovative solution to the same challenge. It proposes a mechanism to establish secure communication between the smart IoT devices and the connected cloud servers through a secure mutual authentication and key establishment process with a short name “SLA-SCA”. The security analysis of the proposed SLA-SCA is done, which proves its resilience against the various potential attacks of such a communication. The proposed SLA-SCA outperformed the other existing schemes regarding important security and functionality features.

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MagLive: Robust Voice Liveness Detection on Smartphones Using Magnetic Pattern Changes

arXiv

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arXiv 2024年

作者： Sun, Xiping Chen, Jing Wu, Cong He, Kun Xu, Haozhe Feng, Yebo Du, Ruiying Chen, Xianhao Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan430072 China Rizhao Institute of Information Technology Wuhan University Rizhao276800 China School of Computer Science and Engineering Nanyang Technological University Singapore Collaborative Innovation Center of Geospatial Technology Wuhan430079 China The Department of Electrical and Electronic Engineering HKU Musketeers Foundation Institute of Data Science University of Hong Kong Pok Fu Lam Hong Kong

Voice authentication has been widely used on smartphones. However, it remains vulnerable to spoofing attacks, where the attacker replays recorded voice samples from authentic humans using loudspeakers to bypass the voice authentication system. In this paper, we present MagLive, a robust voice liveness detection scheme designed for smartphones to mitigate such spoofing attacks. MagLive leverages the differences in magnetic pattern changes generated by different speakers (i.e., humans or loudspeakers) when speaking for liveness detection, which are captured by the built-in magnetometer on smartphones. To extract effective and robust magnetic features, MagLive utilizes a TF-CNN-SAF model as the feature extractor, which includes a time-frequency convolutional neural network (TF-CNN) combined with a self-attention-based fusion (SAF) model. Supervised contrastive learning is then employed to achieve user-irrelevance, device-irrelevance, and content-irrelevance. MagLive imposes no additional burden on users and does not rely on active sensing or specialized hardware. We conducted comprehensive experiments with various settings to evaluate the security and robustness of MagLive. Our results demonstrate that MagLive effectively distinguishes between humans and attackers (i.e., loudspeakers), achieving an average balanced accuracy (BAC) of 99.01% and an equal error rate (EER) of 0.77%. Copyright © 2024, The Authors. All rights reserved.

关键词： Smartphones

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Detection of Crucial Power Side Channel Data Leakage in Neural Networks

Detection of Crucial Power Side Channel Data Leakage in Neur...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Amjed Abbas Ahmed Mohammad Kamrul Hasan Nurhizam Safie Mohd Satar Nazmus Shaker Nafi Azana Hafizah Aman Shayla Islam Saif Aamer Fadhil Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Integrated Work Package KBR Southbank Victoria Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Neural network (NN) accelerators are now extensively utilized in a range of applications that need a high degree of security, such as driverless cars, NLP, and image recognition. Due to privacy issues and the high cost, hardware implementations contained within NN Propagators were often not accessible for general populace. Additionally with power and time data, accelerators also disclose critical data by electro-magnetic (EM) sided channels. Within this study, we demonstrate a side-channel information-based attack that can successfully steal models from large-scale NN accelerators deployed on real-world hardware. The use of these accelerators is widespread. The proposed method of attack consists of two distinct phases: 1) Using EM side-channel data to estimate networking's underlying architecture; 2) Using margin-dependent, attackers learning actively in estimating parameters, notably weights. Deducing the underlying network structure from EM sidechannel data. Inferring the underlying network structure from EM sidechannel data. Experimental findings demonstrate that the disclosed attack technique can be used to precisely retrieve the large-scale NN via the use of EM side-channel information leaking. Overall, our attack shows how critical it is to conceal electromagnetic (EM) traces for massive NN accelerators in practical settings.

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Google Map-based Password Authentication Systems Using Tolerant Distance and Homomorphic Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhou, Zhili Yang, Ching-Nung Wang, Shaowei Nan, Guoshun Cimato, Stelvio Zheng, Yifeng Wang, Qian Guangzhou University Institute of Artificial Intel-ligence China Guangdong Key Labor-atory of Blockchain Security Guangdong510006 China National Dong Hwa University Department of Computer Science and Information Engineering Hualian97401 Taiwan national engineering research center for mobile network technologies Beijing100876 China Universitá degli studi di Milano Italy The Hong Kong Polytechnic University Department of Electrical and Electronic Engi-neering Hong Kong Wuhan University School of Cyber Science and Engineering Wuhan430072 China

Passwords are widely used for authentication in Internet applications. Recently, users tend to adopt graphical passwords instead of traditional alphanumeric passwords, since it is much easier for humans to remember images than verbal representations. However, the existing graphical password authentication systems generally suffer from three main issues. 1) It is required to remember and perform complicated operations during the registration/login phases, which significantly limits the systems' usability;2) The users' passwords are simply stored as plaintexts in servers, and thus the security is compromised;3) The users need to register/login to each server separately when they are applied in multi-server environment. To address the above issues, we propose a user-friendly and secure Google map-based graphical password (FS-GMGP) system using tolerant distance and homomorphic encryption. By using a homomorphic encryption scheme, each user encrypts his password point and response point selected on Google map, while the servers compute and decrypt the distance between the two encrypted points and then compare the resulting value with a tolerant distance for authentication. Moreover, the FS-GMGP system is extended for multi-server environment. The evaluation results and security analysis show that the FS-GMGP and its extended version achieve desirable usability and security in single-server environment and multi-server environment, respectively © 2025 IEEE.

关键词： Authentication Protocol

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G2Face: High-Fidelity Reversible Face Anonymization via Generative and Geometric Priors

arXiv

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arXiv 2024年

作者： Yang, Haoxin Xu, Xuemiao Xu, Cheng Zhang, Huaidong Qin, Jing Wang, Yi Heng, Pheng-Ann He, Shengfeng The School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology State Key Laboratory of Subtropical Building and Urban Science China The Centre for Smart Health The Hong Kong Polytechnic University Hong Kong The School of Future Technology South China University of Technology Guangzhou China The School of Computer Science and Network Security Dongguan University of Technology Dongguan China The Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong The School of Computing and Information Systems Singapore Management University Singapore

Reversible face anonymization, unlike traditional face pixelization, seeks to replace sensitive identity information in facial images with synthesized alternatives, preserving privacy without sacrificing image clarity. Traditional methods, such as encoder-decoder networks, often result in significant loss of facial details due to their limited learning capacity. Additionally, relying on latent manipulation in pre-trained GANs can lead to changes in ID-irrelevant attributes, adversely affecting data utility due to GAN inversion inaccuracies. This paper introduces G2Face, which leverages both generative and geometric priors to enhance identity manipulation, achieving high-quality reversible face anonymization without compromising data utility. We utilize a 3D face model to extract geometric information from the input face, integrating it with a pre-trained GAN-based decoder. This synergy of generative and geometric priors allows the decoder to produce realistic anonymized faces with consistent geometry. Moreover, multi-scale facial features are extracted from the original face and combined with the decoder using our novel identity-aware feature fusion blocks (IFF). This integration enables precise blending of the generated facial patterns with the original ID-irrelevant features, resulting in accurate identity manipulation. Extensive experiments demonstrate that our method outperforms existing state-of-the-art techniques in face anonymization and recovery, while preserving high data utility. Code is available at https://***/Harxis/G2Face. Copyright © 2024, The Authors. All rights reserved.

关键词： Anonymity

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Design of Lightweight Cryptography based Deep Learning Model for Side Channel Attacks

Design of Lightweight Cryptography based Deep Learning Model...

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Australasian Telecommunication Networks and Applications Conference, ATNAC

作者： Amjed Abbas Ahmed Mohammad Kamrul Hasan Nazmus Shaker Nafi Azana Hafizah Aman Shayla Islam Saif Aamer Fadhil Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Techniques Engineering Imam Al-Kadhum College (IKC) Baghdad Iraq Integrated Work Package KBR Southbank Victoria Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia

Depending on a device's encryption mechanism, a wide variety of tangible details could be exposed. These leaks are used in side-channel analysis, which is used to get keys. Due to deep learning's sensitivity to the characteristics of the data being processed, using such algorithms can significantly improve the accuracy and efficiency of side channel analysis. However, classic neural networks are now used for the vast majority of the work that is being done. When the number of nodes in a network grows, so does the efficiency with which key recovery can function. However, the method's computing complexity grows in direct proportion. Overfitting, inadequate capacity for feature extraction, and inefficient training are all potential issues. In this study, we develop a compact convolutional neural network by enhancing a previously existing combination of neural networks. Novel neural network along with previous neural network both have their own implementations of the side-channel analysis used in comparative trials. Statistically, the new network has better accuracy, quicker convergence, and more robustness. As part of the research, heatmaps were provided as a means of data visualisation. The critical interval concentration is higher and the heat value is higher in the new network. Conventional neural networks, which serve as the foundation for various kinds of neural networks, perform much worse than side channel studies based on feature fusion networks.

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