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A data representation method using distance correlation

Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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ERBM: A Machine Learning-Driven Rule-Based Model for Intrusion Detection in IoT Environments

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Computers, Materials and Continua 2025年第3期83.0卷 5155-5179页

作者： Mehmmod, Arshad Batool, Komal Sajid, Ahthsham Alam, Muhammad Mansoor Khan, Inam Ullah Department of Information Security and Data Science Riphah Institute of Systems Engineering Riphah International University Islamabad 46000 Pakistan Faculty of Computing Riphah International University Islamabad 46000 Pakistan Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

Traditional rule-based Intrusion Detection Systems (IDS) are commonly employed owing to their simple design and ability to detect known threats. Nevertheless, as dynamic network traffic and a new degree of threats exist in IoT environments, these systems do not perform well and have elevated false positive rates—consequently decreasing detection accuracy. In this study, we try to overcome these restrictions by employing fuzzy logic and machine learning to develop an Enhanced Rule-Based Model (ERBM) to classify the packets better and identify intrusions. The ERBM developed for this approach improves data preprocessing and feature selections by utilizing fuzzy logic, where three membership functions are created to classify all the network traffic features as low, medium, or high to remain situationally aware of the environment. Such fuzzy logic sets produce adaptive detection rules by reducing data uncertainty. Also, for further classification, machine learning classifiers such as Decision Tree (DT), Random Forest (RF), and Neural Networks (NN) learn complex ways of attacks and make the detection process more precise. A thorough performance evaluation using different metrics, including accuracy, precision, recall, F1 Score, detection rate, and false-positive rate, verifies the supremacy of ERBM over classical IDS. Under extensive experiments, the ERBM enables a remarkable detection rate of 99% with considerably fewer false positives than the conventional models. Integrating the ability for uncertain reasoning with fuzzy logic and an adaptable component via machine learning solutions, the ERBM system provides a unique, scalable, data-driven approach to IoT intrusion detection. This research presents a major enhancement initiative in the context of rule-based IDS, introducing improvements in accuracy to evolving IoT threats. Copyright © 2025 The Authors.

关键词： fuzzy prediction intrusions IoT Rule based

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Flexible Privacy-Preserving data computing With Bilateral Access Control for Cloud-Assisted IoT

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 3166-3178页

作者： Zhang, Yijian Chen, Jie Ning, Jianting Chu, Qiaohan Wang, Luping Zhang, Guang University of Wollongong Institute of Cybersecurity and Cryptology School of Computing and Information Technology WollongongNSW2500 Australia Wuhan University Institute for Math & AI Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science 430040 China East China Normal University Software Engineering Institute Shanghai200062 China Suzhou University of Science and Technology School of Electronic and Information Engineering Jiangsu215009 China

Cloud-assisted Internet of Things (IoT) is a new paradigm to compensate for the disadvantage of limited resources in IoT and extend the functional boundary of IoT. How to preserve the data privacy while identifying the data source in cloud-assisted IoT is a huge challenge and many cryptographic primitives such as matchmaking encryption (ME) are hence introduced to reach this goal. However, we observe that these tools fail to deal with some rigorous cases in which the data owner does not want anyone to disclose the entire data and even authorized users can only obtain a specified part. In this article, to address such issue, we design a privacy-preserving data computing system with bilateral access control (PDCS-BAC) for cloud-assisted IoT based on a new primitive called identity-based matchmaking functional encryption (IB-MFE). We give the formal definition and security model of IB-MFE, then present two concrete IB-MFE schemes for inner-product function. Thus, PDCS-BAC manages to simultaneously provide flexible privacy computation with enhanced data privacy, bilateral access control, and data authenticity. Finally, we conduct comprehensive performance evaluations to demonstrate the practicability of our schemes for cloud-assisted IoT. © 2004-2012 IEEE.

关键词： Differential privacy

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Few-Shot Source Separation for IoT Anti-Jamming via Multi-Task Learning and Meta-Learning

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IEEE Internet of Things Journal 2025年第11期12卷 16761-16776页

作者： Guo, Pengcheng Yu, Miao Li, Cheng An, Kang Sun, Yifu Chatzinotas, Symeon Niyato, Dusit National University of Defense Technology Sixty-Third Research Institute China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System China Kyung Hee University College of Electronics and Information Yongin-si17104 Korea Republic of Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Malicious jamming attacks pose a significant threat to the integrity and performance of Internet of Things (IoT) networks. However, many jamming patterns are rare or infrequent, which makes them difficult to counter effectively. This paper addresses the critical issue of anti-jamming (AJ) under few-shot sample conditions in IoT networks. Source separation is a key component of AJ communication. Although deep learning-based source separation has demonstrated significant advantages, it typically requires a large amount of labeled data, which can be impractical in certain environments. To overcome this challenge, we propose two novel schemes that leverage multi-task learning (MTL) and meta-learning (ML) to enhance the model's signal separation capabilities within the constraints of limited sample scenarios. MTL enhances robustness by leveraging shared representations across tasks, while ML allows for rapid adaptation to novel jamming signals with minimal samples. Specifically, we employ a modified separation model, SepFormer, as our baseline and integrate MTL and ML schemes to enable the separation of unknown or few-shot jamming signals. Additionally, we have constructed two datasets encompassing both simulated and real-world environmental data to test and evaluate the performance of the proposed methods. Simulation results demonstrate the superior AJ performance of our schemes, particularly when compared with a direct application of the separation model with few-shot samples. Furthermore, our evaluation of performance across various jamming scenarios and interference-to-signal ratios (ISR) further confirms the effectiveness of our proposed scheme. © 2014 IEEE.

关键词： Zero-shot learning

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Localization-Assisted Fast and Robust Beam Optimization for mmWave Communications

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IEEE Internet of Things Journal 2025年第9期12卷 12383-12396页

作者： Xiao, Qiqi He, Yinghui Yu, Guanding Yuan, Jiantao Yin, Rui Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore Hangzhou City University School of Information and Electrical Engineering Academy of Edge Intelligence Hangzhou310015 China Zhengzhou Digital Industry Institute Hangzhou City University Zhengzhou450046 China Hangzhou City University School of Information and Electrical Engineering Hangzhou310015 China

The millimeter wave (mmWave) communication becomes a key enabler for the future Internet of Things (IoT) due to its capability for supporting high rate and low-latency traffic. However, beamforming in the mmWave band faces issues of low efficiency since the narrow beam of mmWave devices would increase the search delay and overhead. Inspired by this, we utilize localization over sub-6 GHz band to assist the mmWave base station in performing fast and robust adaptive beamforming (RABF). Different from existing works, we focus on the indoor scenario and consider the effects of several practical issues, including localization errors and hardware defects. Specifically, a novel two-step access scheme is proposed. During the first step, we design a novel localization method customized for indoor scenarios, jointly considering the time of flight and angle of arrival. The localization error is further analyzed to determine the mmWave scanning angle and an optimal beamwidth expression is derived in closed-form to maximize system throughput with the considerations of the search delay. Moreover, considering the mismatch of the steering vector caused by the hardware defects, we propose an RABF method in closed-form. Simulation results demonstrate that the proposed scheme can effectively reduce the search delay and realize robust beamforming to enhance the mmWave communication performance. © 2014 IEEE.

关键词： Beamforming

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Machine learning for Internet of Things (IoT) device identification: a comparative study

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PeerJ Computer science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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High-accuracy face recognition with the MRYOLO-SCO algorithm: combining residual networks and YOLOv2 for improved precision

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Signal, Image and Video Processing 2025年第7期19卷 1-12页

作者： Ettiyappan, Anitha palani, Hari kumar Duraipandian, Kavitha Selvaganapathy, Nandhini Department of Information Technology Loyola - ICAM College of Engineering and Technology Nungambakkam Chennai600034 India Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India Department of computer science and engineering Sathyabama Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu603203 India

Face recognition in real-world scenarios presents significant challenges due to variations in lighting conditions, occlusions, pose changes, and low-resolution images. To address these challenges, this study proposes a Modified Residual YOLO Network-based Single Candidate Optimization algorithm, which integrates a Modified Residual Network with YOLOv2 to improve recognition accuracy while maintaining computational efficiency. Unlike conventional YOLO models, the proposed framework introduces bipolar feature extraction, enabling the network to capture both high-level global features and low-level local details, thus improving robustness against occlusions and illumination variations. Atailored cost function is introduced to minimize bounding box localization errors and classification inaccuracies, incorporating weighted penalties to enhance model precision. Additionally, a Single Candidate Optimization algorithm is employed to fine-tune hyperparameters, ensuring faster convergence and lower computational complexity. The performance of the proposed technique is evaluated using the benchmark datasets and the simulation results that the proposed framework outperforms existing YOLO-based models, achieving an accuracy of 98.8%, a recall of 98.0%, and an F1-score of 98.1%while maintainingalower computational cost than YOLOv3, YOLOv4, and YOLOv5. The experimental results also indicate that the tailored cost function reduces misclassification errors by 6.1% and accelerates convergence by 20%, making the proposed framework highly suitable for real-time face recognition applications in resource-constrained environmentssuch asIoT-based surveillance and smart security systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Face recognition

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