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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Zhuowei Shen Xiao Kou Taiyao Yang Haoqin Xu Dongbin Wang Shaobo Niu School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Intergration Minster of Education Nanjing China School of Cyberspace Security Beijing University of Post and Telecommunications Beijing China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Certificateless public key cryptography (CL-PKC) has garnered significant attention in recent years due to its ability to address the complex certificate management requirements inherent in public key infrastructure (PKI) systems and the key escrow issue associated with identity-based cryptography (IBC). However, the efficient revocation of illegitimate users in authentication schemes based on CL-PKC remains a challenge. Moreover, certain schemes still exhibit security vulnerabilities, such as susceptibility to key replacement attacks, and also require performance enhancements. In this article, we propose a novel certificateless signature scheme that incorporates the RSA accumulator. By leveraging the witness in the RSA accumulator to produce the public keys of users, the authenticity of the public key can be efficiently verified, thereby eliminating key replacement attacks. Additionally, by removing specific elements from the RSA accumulator and broadcasting the update credential to legitimate users via a public channel, our proposed scheme allows the effective revocation of malicious users. security analysis and performance evaluations indicate that our scheme provides comprehensive security and achieves high performance in terms of computational and communication costs in both signingverification and revocation processes.

关键词： Performance evaluation Privacy Costs Identity-based encryption Authentication Broadcasting Forgery Encryption security Digital signatures

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A Survey of Output Feedback Robust MPC for Linear Parameter Varying Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1717-1751页

作者： Xubin Ping Jianchen Hu Tingyu Lin Baocang Ding Peng Wang Zhiwu Li the Department of Automation School of Electro-Mech-anical EngineeringXidian UniversityXi’an 710071China the Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringFaculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China the State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China the College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China the Information and Navigation College Air Force Engineering UniversityXi’an 710077China IEEE the School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071 the Institute of System Engineering Macao University of Science and TechnologyMacao SARChina

For constrained linear parameter varying(LPV)systems,this survey comprehensively reviews the literatures on output feedback robust model predictive control(OFRMPC)over the past two decades from the aspects on motivations,main contributions,and the related *** to the types of state observer systems and scheduling parameters of LPV systems,different kinds of OFRMPC approaches are summarized and *** extensions of OFRMPC for LPV systems to other related uncertain systems are also *** methods of dealing with system uncertainties and constraints in different kinds of OFRMPC optimizations are *** issues on OFRMPC optimizations for LPV systems are ***,the future research directions on OFRMPC for LPV systems are suggested.

关键词： Linear parameter varying(LPV)systems model predictive control(MPC) output feedback robust control

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6G-Enabled Edge AI for Metaverse:Challenges, Methods,and Future Research Directions

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Journal of Communications and Information networks 2022年第2期7卷 107-121页

作者： Luyi Chang Zhe Zhang Pei Li Shan Xi Wei Guo Yukang Shen Zehui Xiong Jiawen Kang Dusit Niyato Xiuquan Qiao Yi Wu School of Data Science and Technology Heilongjiang UniversityHarbin 150080China Institute for Cryptology and Network Security Heilongjiang UniversityHarbin 150080China SenseTime Group Limited Shenzhen 518000China Pillar of Information Systems Technology and Design Singapore University of Technology and Design.Singapore 487372Singapore School of Automation.Guangdong University of Technology and Center for Intelligenl Batch Manufacturing based on IoT TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798.Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications.Beijing 100876China

Sixth generation(6G)enabled edge intelligence opens up a new era of Internet of everything and makes it possible to interconnect people-devices-cloud anytime,*** and more next-generation wireless network smart service applications are changing our way of life and improving our quality of *** the hottest new form of next-generation Internet applications,Metaverse is striving to connect billions of users and create a shared world where virtual and reality ***,limited by resources,computing power,and sensory devices,Metaverse is still far from realizing its full vision of immersion,materialization,and *** this end,this survey aims to realize this vision through the organic integration of 6G-enabled edge artificial intelligence(AI)and ***,we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in *** we summarize technical challenges that these architectures face in Metaverse and the existing ***,we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital ***,we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

关键词： edge artificial intelligence artificial intelli-gence 6G metaverse federated learning

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs 7

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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7th IEEE International Conference on Electronics and Communication Engineering, ICECE 2024

作者： Xu, Mengying Zu, Yunxiao Zhou, Jie Dai, Jiapeng Chen, Jing Liu, Meiru School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Shihezi University Department of Information Science and Technology Shihezi China

ISBN: (纸本)9798331517755

Flying ad hoc networks (FANETs) are composed of multiple UA V nodes that are distributed across a wide area and linked via wireless communication to gather and transmit environmental data. However, the battery-powered UAVs in FANETs imposes significant energy limitations, thereby shortening the network's operational lifespan. This paper proposes an energy-efficient clustering protocol based on improved hybrid grey wolf optimization (EEC-IHGWO), which can optimally select cluster heads (CHs), significantly extend network lifetime and maximize energy efficiency. The selection criteria include factors like remaining energy, the distance from cluster members to CHs, and node density. The performance of the EEC-IHGWO method, measured by network lifetime, the number of living nodes, and remaining energy, is assessed against traditional algorithms such as LEACH, as well as the more recent PSO and FCM. The simulation results demonstrate that EEC-IHGWO sustains a longer network lifetime and higher energy efficiency. © 2024 IEEE.

关键词： Ad hoc networks

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A Survey of Deep Visual Cross-Domain Few-Shot Learning

arXiv

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arXiv 2023年

作者： Wang, Wenjian Duan, Lijuan Wang, Yuxi Fan, Junsong Gong, Zhi Zhang, Zhaoxiang Faculty of Information Technology Beijing University of Technology China Beijing Key Laboratory of Trusted Computing Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China Centre for Artificial Intelligence and Robotics HKISI_CAS Institute of Automation Chinese Academy of Sciences [NLPR CASIA UCAS China

Few-Shot transfer learning has become a major focus of research as it allows recognition of new classes with limited labeled data. While it is assumed that train and test data have the same data distribution, this is often not the case in real-world applications. This leads to decreased model transfer effects when the new class distribution differs significantly from the learned classes. Research into Cross-Domain Few-Shot (CDFS) has emerged to address this issue, forming a more challenging and realistic setting. In this survey, we provide a detailed taxonomy of CDFS from the problem setting and corresponding solutions view. We summarise the existing CDFS network architectures and discuss the solution ideas for each direction the taxonomy indicates. Furthermore, we introduce various CDFS downstream applications and outline classification, detection, and segmentation benchmarks and corresponding standards for evaluation. We also discuss the challenges of CDFS research and explore potential directions for future investigation. Through this review, we aim to provide comprehensive guidance on CDFS research, enabling researchers to gain insight into the state-of-the-art while allowing them to build upon existing solutions to develop their own CDFS models. Copyright © 2023, The Authors. All rights reserved.

关键词： network architecture

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Mengying Xu Yunxiao Zu Jie Zhou Jiapeng Dai Jing Chen Meiru Liu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Department of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

关键词： Wireless communication Protocols Machine learning algorithms Simulation Clustering algorithms Energy efficiency Ad hoc networks Magnetic heads Particle swarm optimization Optimization

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Adversarial Training Via Multi-Guidance and Historical Memory Enhancement

SSRN

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SSRN 2024年

作者： Zhao, Chenyu Qian, Yaguan Wang, Bin Gu, Zhaoquan Ji, Shouling Wang, Wei Zhang, Yanchun School of Science Zhejiang University of Science and Technology China Network and Data Security China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Zhejiang University China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University China Institute for Sustainable Industries and Liveable Cities Victoria University Australia Peng Cheng Laboratory China

DNNs are often susceptible to the influence of adversarial examples, potentially leading to severe security issues. Adversarial training stands out as one of the most effective defenses. In this paper, we empirically demonstrate that weakly robust models maintain resilience to adversarial examples generated on a fully trained robust model. We propose a novel weight ensemble training method called Guided Adversarial Training (GAT). This method enhances both clean and adversarial accuracy through two guiding approaches (Self-Guidance and Expert-Guidance), as well as by improving the historical memory of member models' weights. The experiments prove that our method is effective in defending against most adversarial attacks. It emphasizes the need to maintain the diversity of decision boundaries of member models while improving the alignment with natural decision boundaries when ensemble the model weights. The code will be released after acceptance. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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APTNER: A Specific Dataset for NER Missions in Cyber Threat Intelligence Field 25

APTNER: A Specific Dataset for NER Missions in Cyber Threat ...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Xuren He, Songheng Xiong, Zihan Wei, Xinxin Jiang, Zhengwei Chen, Sihan Jiang, Jun Capital Normal University Information Engineering College Beijing China Xi'an Jiaotong University School of Materials Science and Engineering Xi'an China Chinese Academy of Sciences Key Laboratory of Network Assessment Technology Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9781665405270

This paper provides a new dataset for Named Entity Recognition (NER) missions in cyber threat intelligence (CTI) studying. To the best of our knowledge, the proposed dataset is the biggest and challenging one in the field to comply with the STIX 2.1 specification. We collected the APT (Advanced Persistent Threats) reports from different network security companies and manually annotated them. Then we constructed a dataset named APTNER, which can be used for NER joint and multi-task learning tasks in CTI. Apart from common labels like IP, URL, mal-ware, location and so on, APTNER contains 21 categories, which make APTNER more challenging than other NER datasets in CTI field and we have proved the rationality of the dataset. For ease of comparison studies, we realize several state-of-the-art baselines and report their analysis. To facilitate future work on fine-grained NER for CTI, we make APTNER public at https://***/wangxuren/APTNER. © 2022 IEEE.

关键词： network security

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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Poisoning Attacks and Defenses to Learned Bloom Filters for Malicious URL Detection

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Dong, Fangming Wang, Pinghui Li, Rundong Cui, Xueyao Zhao, Junzhou Tao, Jing Zhang, Chen Guan, Xiaohong Zhejiang CreateLink Technology Co. Ltd Guangdong Guangzhou China Xi'an Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Shaanxi Xi'an710049 China Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing China

Approximate membership query (AMQ) structures represented by the Bloom Filter and its variants have been popularly researched in recent years. Researchers have recently combined machine learning with this type of structure to reduce space consumption and computation overhead further and make remarkable progress. However, with the booming performance in space or other metrics, researchers tend to ignore the security of the trained model. The machine learning model is vulnerable to poisoning attacks, and naturally, we infer that the learning-based filters also have the same deficiencies. Hence, in this paper, to confirm the inference mentioned above, experiments on the real-world datasets of URLs are conducted and prove that it is necessary to consider the security issue when using learning-based filters. We show that by data poisoning, the attacker can deflect learned Bloom Filters to make a false identification, which can lead to a significant loss in some cases. Aiming to solve this issue, we put forward a method named Defensive Learned Bloom Filter (DLBF) to diminish the influence of data poisoning and achieve a better performance compared to types of learned Bloom Filters. © 2004-2012 IEEE.

关键词： Wiener filtering

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