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17th International Conference on security of information and Networks, SIN 2024

作者： Ghazal, Taher M. Janjua, Jamshaid Iqbal Abushiba, Walid Ahmad, Munir Ihsan, Anuam Al-Dmour, Nidal A. College of Arts & Science Applied Science University Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia Lahore Pakistan College of Engineering Applied Science University Bahrain College of Informatics Korea University Seoul02841 Korea Republic of College of Engineering Mutah University Department of Computer Engineering Jordan

ISBN: (纸本)9798331509736

Integrating Groundbreaking advancements in AI, like language models, interpretable AI, and machine learning, opens up a world of exciting new possibilities. The Evolving face of cybersecurity and Modern cyber threats are complex and well crafted;hence, conventional cybersecurity mechanisms show difficulty in staying relevant. LLMs, especially based on Transformer architecture will noticeably increase the accuracy and speed of detecting threats. Transparency and trust are increased by XAI approaches like SHAP and LIME, which offer facts about ML model predictions. This paper explores the literature that demonstrates the integration between XAI and LLMs in cybersecurity, exemplifying how this trinity of models has the potential to help attenuate errors producing reduced false positives and improve how we detect threats. Thinking about the possibilities the challenges including performance Explainability trade-offs, the need for common evaluation metrics, and the black-box nature of AI Models, remain in place. Solving these will help to enhance AI-driven solutions in cybersecurity. © 2024 IEEE.

关键词： Cybersecurity Explainable AI (XAI) Large Language Models (LLMs) Machine Learning (ML) Reliability Threat Detection Transformer Architecture Transparency

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Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation

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IEEE Transactions on Cybernetics 2024年第8期54卷 4643-4651页

作者： Jaw, Da-Wei Huang, Shih-Chia Lu, Zhi-Hui Fung, Benjamin C. M. Kuo, Sy-Yen National Taiwan University Department of Electrical Engineering Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan Fudan University School of Computer Science The Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education Shanghai200433 China McGill University School of Information Studies MontrealQCH3A 1X1 Canada

Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with sufficient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low- and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low- and sufficient-luminance domains. © 2013 IEEE.

关键词： Feature extraction

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Quantum Remote State Preparation Based on Quantum Network Coding

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computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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Sockpuppets Detection Using Markov-Driven Graph Convolutional Networks with Ensemble Learning 7

Sockpuppets Detection Using Markov-Driven Graph Convolutiona...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Fan, Chun-I Tsai, Ming-Feng Hu, Chun-Pin Wang, Ming-Hung National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung804 Taiwan Intelligent Electronic Commerce Research Center National Sun Yat-sen University Kaohsiung804 Taiwan National Chung Cheng University Departrment of Computer Science and Information Engineering Chiayi621 Taiwan

ISBN: (纸本)9798331540289

This paper introduces a novel framework for detecting sockpuppets in online social networks, utilizing an advanced approach that integrates Markov-driven Graph Convolutional Networks (MDGCN) with ensemble learning techniques. Our method capitalizes on the data inherent in social networks by applying GCNs to capture and learn from intricate user interactions. To address the class imbalance problem often encountered in sockpuppet detection, we incorporate Focal Loss into our training regimen, enhancing the model's capability to identify less frequent, harder-to-detect instances of false information spreaders. Additionally, we implement ensemble learning strategies to refine and bolster the predictive power of our model. Through extensive testing on multiple social network platform datasets, our proposed method demonstrates superior performance in identifying sockpuppets compared to existing techniques. This underscores its potential as a robust solution for safeguarding against the rapid spread of false information online. © 2024 IEEE.

关键词： Markov processes

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PUF-Based Privacy-Preserving Simultaneous Authentication Among Multiple Vehicles in VANET

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IEEE Transactions on Vehicular Technology 2024年第5期73卷 6727-6739页

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Akram, Muhammad Wahid Hussain, Shafiq Das, Ashok Kumar Park, Youngho Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Lahore Department of Software Engineering Punjab Lahore54590 Pakistan University of Sahiwal Department of Computer Science Punjab Sahiwal57000 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Telangana Hyderabad500032 India Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of

The intellectual and immense attainment of a smart city and auto-mobile vehicles are gradually growing. This growth drives Vehicular Ad-Hoc Network (VANET), a broadly operated complex network for inter-vehicular transmission to acquire information relevant to the vehicle location, road condition, traffic congestion, and speed. Generally, this information is procured through an open-source communication, which is susceptible to various security threats. In broad terms, the privacy of data in VANET is considered an imperative task. In this regard, many authentication protocols are formerly initiated for VANET. Most of these protocols are precarious owing to eavesdropping, replay, and impersonation attacks, etc. Furthermore, most existing protocols operates on enormous communication and computation rate, which turns them inadequate for VANET. This paper exhibits a lightweight privacy-preserving authentication protocol for n number of vehicles in VANET using the physical unclonable function (PUF) technique, and offers efficient and secure communication via public channels. Moreover, we evaluate our protocol formally and informally, showing that it is scalable, lightweight, and safe compared to formerly protocols. The rate of computation and communication is less than most related protocols. We also simulate the proposed scheme using the NS-3 simulator to measure its various network performance parameters. As a result, it is apparent through the beheld statistics that the presented protocol is beyond practical, efficient, and reliable for the secure dissemination of information in VANET. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Another Lattice Attack Against ECDSA with the wNAF to Recover More Bits per Signature 1

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18th EAI International Conference on security and Privacy in Communication Networks, SecureComm 2022

作者： Ma, Ziqiang Li, Shuaigang Lin, Jingqiang Cai, Quanwei Fan, Shuqin Zhang, Fan Luo, Bo School of Information Engineering Ningxia University Yinchuan China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Science and Technology of China Hefei China State Key Laboratory of Cryptology Beijing China School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou310027 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou310027 China Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

In the resource-constrained environment such as the Internet of Things, the windowed Non-Adjacent-Form (wNAF) representation is usually used to improve the calculation speed of the scalar multiplication of ECDSA. This paper presents a practical cache side channel attack on ECDSA implementations which use wNAF representation. Compared with existing works, our method exploits more information from the cache side channels, which is then efficiently used to construct lattice attacks in the ECDSA private key recovery. First, we additionally monitor the invert function which is related to the sign of the wNAF digits, and obtain a Double-Add-Invert chain through the Flush+Flush cache side channel. Then, we develop effective methods extracting 154.2 bits information of the ephemeral key per signature for 256-bit ECDSA from this chain, much more than the best known result which extracts 105.8 bits per signature. Finally, to efficiently use the extracted information, we convert the problem of recovering the private key to the Hidden Number Problem (HNP) and the Extended Hidden Number Problem (EHNP) respectively, which are solved by lattice reduction algorithms. We applied the attack on ECDSA with the secp256k1 curve in OpenSSL 1.1.0h. The experimental results show that only 3 signatures are enough to recover the private key. To the best of our knowledge, this work exploits the signs of the wNAF representation, along with the Double-Add chain against ECDSA, to recover the private key with the least number of signatures. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Side channel attack

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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Software-Defined Networking Integrated with Cloud Native and Proxy Mechanism: Detection and Mitigation System for TCP SYN Flooding Attack

Software-Defined Networking Integrated with Cloud Native and...

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17th International Conference on Ubiquitous information Management and Communication, IMCOM 2023

作者： Fan, Chun-I Wang, Jun-Huei Shie, Cheng-Han Tsai, Yu-Lung National Sun Yat-sen University Department of Computer Science and Engineering Information Security Research Center Intelligent Electronic Commerce Research Center Kaohsiung Taiwan Information and Communication Security Technical Laboratories Telecommunication Laboratories Chunghwa Telecom Taoyuan Taiwan

ISBN: (纸本)9781665453486

SDN is a new network architecture that still faces traditional network attacks. Among the attacks, Distributed-Denial-of-Service is one of the most severe attacks, especially the TCP SYN Flooding attack, which has a more significant impact. This paper is mainly to mitigate the impact of the SYN Flood attack and will combine Kubernetes and design a unique component: SDN Controller Manager, which exists in K8s as a microservices. The proxy mechanism is used to resist the SYN Flooding attack, and the K8s feature is further utilized. If the SDN Controller Manager is attacked and the service is terminated abnormally, K8s will automatically create a new SDN Controller Manager component to continue to provide services. © 2023 IEEE.

关键词： Controllers

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AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

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Intelligent Automation & Soft Computing 2023年第4期36卷 1095-1104页

作者： Liaqat Ali Saif E.A.Alnawayseh Mohammed Salahat Taher M.Ghazal Mohsen A.A.Tomh Beenu Mago College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE Electrical Engineering Department Faculty of EngineeringMutah UniversityJordan School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia43600Malaysia Faculty of Computer Science NCBA&ELahore54660Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent *** an outbreak occurs,each country must have a well-coordinated and preventative plan to address the *** and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily *** intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern ***,we used an artiﬁcial intelligence AI-based system for disease prediction using an Artiﬁcial Neural Network(ANN).The ANN improved the regularization of the classiﬁcation model,hence increasing its *** unconstrained opti-mization model reduced the classiﬁer’s cost function to obtain the lowest possible *** verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed *** proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

关键词： Intelligent model epidemics artiﬁcial intelligence machine learning techniques

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