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Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs

Transactions on Emerging Telecommunications Technologies 2025年第6期36卷

作者： Aadam Quraishi Rakeshnag Dasari Sushilkumar Dangiya Sateesh Kumar Nallamala Krishna Kanth Kondapaka Swaroop Reddy Gayam Isa Bayhan Uguloy Berdieva Rubal Jeet M.D. Research Cognitive and AI laboratory Interventional treatment institute Houston Texas USA Department of CSE Acharya Nagarjuna University Nagarjuna Nagar Guntur India Department of Electronics and Communication Engineering GTU – School of Engineering and Technology Ahmedabad India Independent Researcher USA Independent Researcher California USA Independent Researcher and Senior Software Engineer at TJMax USA Assistant Professor of Tourist Guiding Dept Bolu Abant Izzet Baysal University Golkoy Campus Bolu Turkey The Department of Tax and Taxation Tashkent State University of Economics Tashkent Uzbekistan Chandigarh Group of Colleges Jhanjeri Chandigarh Engineering College Department of Computer Science and Engineering Punjab India

Determining the weights of evaluation metrics is one of the key factors influencing the cybersecurity and anomaly detection of intelligent vehicular ad hoc networks (VANETs). To address the limitations of traditional weighting methods, which often overlook the impact of changes in metric attribute states on evaluation weights, this paper proposes a dynamic weight allocation–based network security and anomaly detection model. The model begins by decomposing and analyzing the security and anomaly detection objectives of VANETs, constructing a comprehensive evaluation metric system. The network security assessment model for VANETs presented in this research overcomes the drawbacks of conventional static models by utilizing a dynamic weight allocation technique. Based on current network conditions, a state variable weight method was created that dynamically computes security values by combining incentive and penalty mechanisms. A ranking-based weighting algorithm is employed to analyze the correlation between security and anomaly detection metrics. Subsequently, the proposed dynamic weight allocation algorithm calculates the dynamic weights of individual metrics within the system, enabling a robust assessment of network security and anomaly detection for intelligent VANETs. The evaluation results provide security level classifications and identify anomalies effectively. Experimental results demonstrate that the model significantly enhances the rationality and accuracy of intelligent VANET evaluations, contributing to improved cybersecurity and anomaly detection.

关键词： anomaly detection dynamic weight allocation intelligent vehicle network security VANETs

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Topology Sequences-based Fuzzy Track Initialization Method For Radars and ADS-B*

Topology Sequences-based Fuzzy Track Initialization Method F...

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Asian Control Conference

作者： Xiaobin Li Hongfeng Ma En Fan College of Software Engineering Lanzhou Institute of Technology Lanzhou China College of Electronic and Information Engineering Lanzhou Institute of Technology Lanzhou China Department of Computer Science and Engineering Shaoxing University Shaoxing China

ISBN: (纸本)9781728102634

Radars and automatic dependent surveillance -broadcast (ADS-B) are two important surveillance technologies in aerial surveillance networks. For this situations with different systematic errors of 2D radars and their time-difference-arrival (TOA) measurements in the fusion center, a fuzzy track initialization method based on topology sequences (TS-FTI) for heterogeneous sensors is proposed. In this proposed method, topology sequences of various measurements from a radar and an ADS-B receiver are respectively constructed. Then the topology sequence method is utilized to classify these measurements. The measurement sets of candidate targets can be constructed by accumulating measurements in several successive sampling intervals. Finally, a fuzzy Hough transform (FHT) method is used to detect tracks. The performance of the TS-FTI method is evaluated by simulation experiments, and it is found to be better than that of the track initialization method based on standard Hough transform (HT-TI) in calculated complexity and correct initialization rate.

关键词： Radar tracking Topology Receivers Radar measurements Target tracking Surveillance

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Application of quantisation-based deep-learning model compression in JPEG image steganalysis

Application of quantisation-based deep-learning model compre...

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作者： Wu, Xiancheng Shao, Zilong Ou, Pei Tan, Shunquan College of Information Engineering Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Steganography can hide secret information in an innocent cover medium. Its opponent is steganalysis, which is used to discriminate whether a suspicious carrier contains a hidden message or not. With the rapid development of deep-learning frameworks, deep-learning-based steganalytic models have hold the dominant position in the field of steganalysis. In recent years, some scholars have successfully utilised model compression methods in the field of image classification. However, as far as the authors know, no prior works are devoted to the application of model compression methods in the field of deep-learning-based steganalysis. In this study, the authors explore the effect of two quantisation schemes, namely 8-bit calculation and floating-point calculation, on the performance of XuNet, a state-of-the-art deep-learning steganalytic model. The experimental results show that the two deep-learning model quantisation schemes are applicable to steganalysis. It is even possible to compress the network size while retaining satisfactory performance. © 2018 Institution of engineering and Technology. All rights reserved.

关键词： Steganography

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Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems

arXiv

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arXiv 2020年

作者： Zhang, Haijun Zhang, Haisen Liu, Wei Long, Keping Dong, Jiangbo Leung, Victor C.M. Institute of Artificial Intelligence Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China China Mobile Group Design Institute Co. Ltd Beijing100080 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBC V6T 1Z4 Canada

Terahertz (THz) band communication has been widely studied to meet the future demand for ultra-high capacity. In addition, multi-input multi-output (MIMO) technique and non-orthogonal multiple access (NOMA) technique with multi-antenna also enable the network to carry more users and provide multiplexing gain. In this paper, we study the maximization of energy efficiency (EE) problem in THz-NOMA-MIMO systems for the first time. And the original optimization problem is divided into user clustering, hybrid precoding and power optimization. Based on channel correlation characteristics, a fast convergence scheme for user clustering in THz-NOMA-MIMO system using enhanced K-means machine learning algorithm is proposed. Considering the power consumption and implementation complexity, the hybrid precoding scheme based on the sub-connection structure is adopted. Considering the fronthaul link capacity constraint, we design a distributed alternating direction method of multipliers (ADMM) algorithm for power allocation to maximize the EE of THz-NOMA cache-enabled system with imperfect successive interference cancellation (SIC). The simulation results show that the proposed user clustering scheme can achieve faster convergence and higher EE, the design of the hybrid precoding of the sub-connection structure can achieve lower power consumption and power optimization can achieve a higher EE for the THz cache-enabled network. Copyright © 2020, The Authors. All rights reserved.

关键词： MIMO systems

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A New Lattice Sieving Algorithm Base on Angular Locality-Sensitive Hashing 13th

A New Lattice Sieving Algorithm Base on Angular Locality-Sen...

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13th International Conference on Information Securityand Cryptology, Inscrypt 2017

作者： Wang, Ping Shang, Dongdong College of Information Engineering Shenzhen University Shenzhen518060 China College of Computer Science and Software Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783319751597

Currently, the space requirement of sieving algorithms to solve the shortest vector problem (SVP) grows as 2 0.2075n+o(n), where n is the lattice dimension. In high dimensions, the memory requirement makes them uncompetitive with enumeration algorithms. Shi Bai et al. presents a filtered triple sieving algorithm that breaks the bottleneck with memory 2 0.1887n+o(n) and time 2 0.481n+o(n). Benefiting from the angular locality-sensitive hashing (LSH) method, our proposed algorithm runs in time 2 0.4098n+o(n) with the same space complexity 2 0.1887n+o(n) as the filtered triple sieving algorithm. Our experiment demonstrates that the proposed algorithm achieves the desired results. Furthermore, we use the proposed algorithm to solve the closest vector problem (CVP) with the lowest space complexity as far as we know in the literature. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Vector spaces

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Emotion Recognition From Gait Analyses: Current Research and Future Directions

arXiv

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arXiv 2020年

作者： Xu, Shihao Fang, Jing Hu, Xiping Ngai, Edith Wang, Wei Guo, Yi Leung, Victor C.M. School of Information Science and Engineering Lanzhou University Gansu730000 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong Shenzhen518107 China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Second Clinical Medical College Jinan University Shenzhen518055 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518055 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker’s emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech, and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques in intelligent computation and big data. Copyright © 2020, The Authors. All rights reserved.

关键词： Emotion Recognition

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MeDaS: An open-source platform as service to help break the walls between medicine and informatics

arXiv

引用

arXiv 2020年

作者： Zhang, Liang Li, Johann Li, Ping Lu, Xiaoyuan Shen, Peiyi Zhu, Guangming Shah, Syed Afaq Bennarmoun, Mohammed Qian, Kun Schuller, Björn W. Embedded Technology & Vision Processing Research Center School of Computer and Technology Xidian University Xian China Shanghai BNC Shanghai China College of Science Health Enginering and Education Murdoch University Australia School of Computer Science and Software Engineering University of Western Australia Australia Educational Physiology Laboratory University of Tokyo Japan GLAM - Group on Language Audio & Music Imperial College London United Kingdom Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields including computer vision, natural language processing, and healthcare. In particular, DL is experiencing an increasing development in applications for advanced medical image analysis in terms of analysis, segmentation, classification, and furthermore. On the one hand, tremendous needs that leverage the power of DL for medical image analysis are arising from the research community of a medical, clinical, and informatics background to jointly share their expertise, knowledge, skills, and experience. On the other hand, barriers between disciplines are on the road for them often hampering a full and efficient collaboration. To this end, we propose our novel open-source platform, i. e., MEDAS–the MeDical open-source platform as Service. To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side. Based on a series of toolkits and utilities from the idea of RINV (Rapid Implementation aNd Verification), our proposed MEDAS platform can implement preprocessing, post-processing, augmentation, visualization, and other phases needed in medical image analysis. Five tasks including the subjects of lung, liver, brain, chest, and pathology, are validated and demonstrated to be efficiently realisable by using MEDAS. Copyright © 2020, The Authors. All rights reserved.

关键词： Clinical research

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Mapping digital forensic application requirement specification to an international standard

引用

Forensic science International: Reports 2020年 2卷 100137-100137页

作者： Kebande, Victor R. Baror, Stacey O. Parizi, Reza M. Choo, Kim-Kwang Raymond Venter, H.S. Department of Computer Science & Media Technology Malmö Universitete Malmö Sweden Department of Computer Science University of Pretoria South Africa College of Computing and Software Engineering Kennesaw State University Marietta GA United States Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio 78249-0631 TX United States

A potential security incident may go unsolved if standardized forensic approaches are not applied during lawful investigations. This paper highlights the importance of mapping the digital forensic application requirement specification to an international standard, precisely ISO/IEC 27043. The outcome of this work is projected to contribute to the problem of secure DF tool creation, and in the process address software Requirements Specification (SRS) as a process of digital evidence admissibility. © 2020 The Author(s)

关键词： Applications design Digital forensics ISO/IEC 27043 software requirements specification (SRS)

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Reducing Wi-Fi Fingerprint Collection Based on Affinity Propagation Clustering and WKNN Interpolation Algorithm 2

Reducing Wi-Fi Fingerprint Collection Based on Affinity Prop...

引用

2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

作者： Hu, Jiusong Liu, Hongli Liu, Dawei Yan, Zhi Xu, Kun College of Electrical and Information Engineering Hunan University China Department of Computer Science Software Engineering Xi' An Jiaotong-Liverpool University China

ISBN: (纸本)9781538618035

The Wi-Fi-based indoor positioning system compares the online signal strength indicator (RSSI) and offline stored fingerprints to find the closest match to estimate the target location of the device. However, the problem is that the process of collecting fingerprints is very laborious, time-consuming and expensive. It is challenging to solve this problem. We proposed a method to reduce fingerprint collection based on APC (affinity propagation clustering) and WKNN (Weighted K-Nearest Neighbor) interpolation algorithm to solve the problem in this paper. We use the APC algorithm to break the known RPs (Reference points) into several clusters. The cluster representative is recorded. Then, we classify the unknown RPs as in the cluster which is physically nearest to cluster representative. Finally, in each cluster, we use fingerprints of known RPs with the WKNN interpolation algorithm to calculate fingerprints of the unknown RPs. We do experiments in a real environment. The results of real environmental experiments show that our method only needs 40% of the fingerprint to restore all the fingerprints in our environment, while the average localization accuracy only lost 8%. It only needs 70% of the fingerprints to restore all the fingerprints almost without any loss of average localization accuracy in our environment. © 2018 IEEE.

关键词： Indoor positioning systems

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Artificial intelligence-enabled intelligent 6G networks

arXiv

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arXiv 2019年

作者： Yang, Helin Alphones, Arokiaswami Xiong, Zehui Niyato, Dusit Zhao, Jun Wu, Kaishun School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (AI) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an AI-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of AI techniques for 6G networks and elaborate how to employ the AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. Moreover, we highlight important future research directions and potential solutions for AI-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management. Copyright © 2019, The Authors. All rights reserved.

关键词： Data mining

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