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A Novel Lightweight IoT Intrusion Detection Model Based on Self-knowledge Distillation

IEEE Internet of Things Journal 2025年第11期12卷 16912-16930页

作者： Wang, Zhendong Zhou, Renqiang Yang, Shuxin He, Daojing Chan, Sammy Jiangxi University of Science and Technology School of Information Engineering Ganzhou341000 China Nanchang University Software Engineering Institute Nanchang330047 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China City University of Hong Kong Department of Electrical Engineering Hong Kong

The Internet of Things (IoT) environment contains many different types of devices, each with different functionalities, communication protocols, and security capabilities, which makes the IoT a complex challenge for security protection. Therefore, network intrusion detection (NID) is needed to detect intrusions in the network to secure the IoT. In recent years, deep learning-based intrusion detection systems have achieved excellent results, but they tend to require high computational resources and storage space, which is not feasible for most IoT devices. In this paper, we propose a lightweight intrusion detection model based on self-knowledge distillation, namely TBCLNN, which improves the detection accuracy while also reducing the number of model parameters and computational cost. Specifically, we use the binary Harris Hawk optimization algorithm for dimensionality reduction of traffic features. We use lightweight convolution, such as Tied Block Convolution (TBC), to design lightweight neural network models with residual and inverse residual structures. Moreover, we propose an improved self-knowledge distillation loss function to solve the sample imbalance problem and compensate for the performance degradation caused by lightweight neural networks. The multiclassification accuracy of our proposed method exceeds 99% on all three publicly available IoT datasets. The experimental results show that our method has a small model size and requires only low computational resources, making it suitable for resource-constrained IoT intrusion detection. © 2014 IEEE.

关键词： Network intrusion

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Quantifying Bytes:Understanding Practical Value of Data Assets in Federated Learning

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Tsinghua Science and technology 2025年第1期30卷 135-147页

作者： Minghao Yao Saiyu Qi Zhen Tian Qian Li Yong Han Haihong Li Yong Qi School of Computer Science and Technology Xi’an Jiaotong UniversityXi’an 710049China School of Cyber Science and Engineering Xi’an Jiaotong UniversityXi’an 710049China

The data asset is emerging as a crucial component in both industrial and commercial *** valuable knowledge from the data benefits decision-making and ***,the usage of data assets raises tension between sensitive information protection and value *** an emerging machine learning paradigm,Federated Learning(FL)allows multiple clients to jointly train a global model based on their data without revealing *** approach harnesses the power of multiple data assets while ensuring their *** the benefits,it relies on a central server to manage the training process and lacks quantification of the quality of data assets,which raises privacy and fairness *** this work,we present a novel framework that combines Federated Learning and Blockchain by Shapley value(FLBS)to achieve a good trade-off between privacy and ***,we introduce blockchain in each training round to elect aggregation and evaluation nodes for training,enabling decentralization and contribution-aware incentive distribution,with these nodes functionally separated and able to supervise each *** experimental results validate the effectiveness of FLBS in estimating contribution even in the presence of heterogeneity and noisy data.

关键词： Federated Learning(FL) blockchain fairness

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Exploring the Microcosm: A Comprehensive Survey of Micro Sensor Applications Across Multidisciplinary Research

Exploring the Microcosm: A Comprehensive Survey of Micro ...

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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： Nandhini, R. Radha, R. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819767090

The main aim of this survey is to provide wider insight about the use of MEMS (Micro-Electro-Mechanical-System) technology in various interdisciplinary fields. The areas include IoT (Internet-Of-Things) for smart automations, agricultural field for performing precision farming. Automotive sector to predict failures, environmental aspects to detect factors affecting ecosystem, food control to measure the quality. Healthcare for identifying the disease in prior stages, robotics to carry out multiple operations. Machine Learning algorithms play vital role in classifying and extracting the necessary feature from the raw data. Deep Learning techniques like ANN (Artificial Neural Network) is employed for estimation of optimal input and output parameters. CNN (Convolutional Neural Network) for representing the designed structure in binary pixelated image form. The various types of sensors, energy harvesters, transducers and actuators which are designed using MEMS technology and their predominant role in recent advancements has been discussed in brief. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automotive industry

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A Frog-Shaped UWB MIMO Antenna Design for 5G

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Progress in Electromagnetics Research C 2025年 151卷 101-112页

作者： Yao, Shanhua Yang, Tianchu Qiu, Xiaorong Li, Xiang School of Electrical and Information Engineering Anhui University of Science and Technology Huainan232001 China School of Computer Science Huainan Normal University Huainan232001 China

In this paper, a frog-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna is proposed for 5G applications in the n77, n78, n79, and 6 GHz bands with a compact antenna structure of 31 × 55 × 1.5 mm3. The designed antenna consists of two frog-shaped monopole antennas and a floor from which a part has been removed, and the operating bandwidths range from 3.05 to 13.38 GHz, which meets the design criteria for UWB. The T-shaped floor with two T-shaped slots impedes the flow of coupling currents and improves the isolation of the antenna. This results in an isolation of less than −17 dB over the entire operating bandwidth and less than −20 dB in the 5G band. In addition, the envelope correlation coefficient (ECC) is less than 0.007, the diversity gain (DG) more than 9.96, the total active reflection coefficient (TARC) less than −30 dB, and the channel capacity loss (CCL) less than 0.34 bit/s/Hz. The measured and simulated results agree with each other, demonstrating the antenna’s potential application in 5G communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Bandwidth

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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

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SN computer Science 2024年第6期5卷 750页

作者： Moeed, Syed Abdul Karnati, Ramesh Ashmitha, G. Mohammad, Gouse Baig Mohanty, Sachi Nandan Department of Computer Science and Engineering Kakatiya Institute of Technology and Science Department of Computer Science and Engineering Vardhaman College of Engineering School of Computer Science & amp Engineering (SCOPE) VIT-AP University

The Internet of Things (IoT) has revolutionized our lives, but it has also introduced significant security and privacy challenges. The vast amount of data collected by these devices, often containing sensitive information, makes them prime targets for cyberattacks. Traditional security methods struggle to keep pace with the evolving threat landscape of the interconnected IoT world. In response, this paper explores the transformative potential of federated learning (FL) in safeguarding both the privacy and security of IoT devices. FL keeps data on individual devices, only sharing updated models, not raw data, thus protecting user privacy and eliminating the need for a central data storage server, which reduces the risk of data breaches and security vulnerabilities. The effectiveness of the proposed model is evaluated using established open-source datasets like NSL-KDD and UNSW NB15, ensuring real-world applicability. Analysis of the dataset's features enabled the development of a model utilizing federated learning. Notably, the proposed model achieved superior performance with FL in detecting attacks on IoT networks. Furthermore, this research investigates the transformative potential of FL to address the inherent security and privacy challenges plaguing traditional, centralized data collection methods in the ever-expanding realm of IoT devices. FL empowers collaborative learning on distributed datasets, allowing devices to collectively improve security without compromising user privacy. The findings of this study demonstrate the promise of FL as a secure and privacy-preserving solution for the future of IoT. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberattacks Federal learning Internet of Things Machine learning Security and privacy

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Enhancing Image Super-Resolution with GANs Featuring Enhanced Deep Super-Resolution and Attention Mechanisms 4

Enhancing Image Super-Resolution with GANs Featuring Enhance...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Kumar, G. Aditya Kativar, Aditi Kannan, A. Vellore Institute of Technology Computer Science and Engineering Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387063

Improving the quality and resolution of low- resolution digital images is an important task with far-reaching implications for a variety of applications, including medical imaging, surveillance, and content retrieval. To solve the high- resolution problem of a single image, this study proposes a new Generative Adversarial Network (GAN) model that includes an Enhanced Deep Super-Resolution (EDSR) generator. Our model includes a sensitivity function and a Squeeze-and- Excitation (SE) block in an EDSR design. These SE blocks directly model channel connections to optimize channel-specific performance responses and improve network capabilities without incurring significant computational costs. After extensive training using complex data processing methods such as rotation, rotation, and rotation, our model shows an impressive ability to enhance image details and traditional culture more accurately. Our strategy outperforms existing models both statistically and qualitatively with better signal-to- noise ratios (PSNRs) on the DIV2K dataset. These results, together with deep learning models, demonstrate the role of attentional processes in improving image resolution and lay the groundwork for further work in this field. © 2024 IEEE.

关键词： Generative adversarial networks

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MCDGMatch: Multilevel Consistency Based on Data-augmented Generalization for Remote Sensing Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12470-12484页

作者： Liu, Pingping Liu, Xiaofeng Liu, Zetong Li, Haodong Zhou, Qiuzhan Jilin University School of Computer Science and Technology Changchun130012 China Jilin University School of Communication Engineering Changchun130012 China

The exponential growth of remote sensing image data and the high cost of manual annotation have led to insufficient labeled data, limiting classification performance. Semisupervised methods can address this issue, but most existing approaches lack multi-level consistency constraints in both embedding space and prediction probabilities, resulting in weak feature expressiveness. Additionally, the fine-grained characteristics of remote sensing images, amplified by data augmentation, are often overlooked, leading to overfitting and inaccurate decision boundaries. To address these issues, we propose a multi-level consistency solution that operates at both the embedding space and prediction probability levels. First, we refine the traditional data augmentation strategy and propose a mutual constraint loss function based on this refinement. Second, to address the intra-class variability of remote sensing images, we introduce a novel metric loss function to constrain the embedding space, taking into account both intra-sample consistency and intersample variability. Experimental results on three remote sensing image datasets demonstrate that our proposed method achieves significant performance e improvements compared to existing semi-supervised methods. © 2008-2012 IEEE.

关键词： Labeled data

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A Personality Detection Method Based on Semantic and Psycholinguistic of Social Texts 7

A Personality Detection Method Based on Semantic and Psychol...

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7th International Conference on computer Information Science and Application technology, CISAT 2024

作者： Wang, Chundong Yang, Yuhan Lin, Hao Han, Lu Tianjin University of Technology School of Computer Science and Engineering National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350375107

At the intersection of personality psychology, computer science, and linguistics, more and more researchers are paying attention to personality detection based on content analysis of texts on social media. However, existing social text-based personality detection methods rely heavily on pre-trained language models, overlook multi-dimensional sentiment in social texts, and lack of effective feature selection of psycholinguistic features. To address these limitations, we propose a method for Myers-Briggs type indicator (MBTI) personality detection. Our approach extracts and selects high-dimensional psycholinguistic feature, utilizes an optimal intermediate layer output from pre-trained language models for semantic representation, and fuses these features to enhance detection accuracy. The experiment results indicate that our proposed method improves the state-of-art results for MBTI personality traits by 1.65% on average on Kaggle MBTI dataset. © 2024 IEEE.

关键词： Psychoacoustic

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Elevating IDS Capabilities: The Convergence of SVM, Deep Learning, and RFECV in Network Security 2

Elevating IDS Capabilities: The Convergence of SVM, Deep Lea...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Kumar, G Aditya Katiyar, Aditi Srinivasan, Kathiravan Computer Science Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

By presenting an improved Intrusion Detection System (IDS) that combines deep learning with support vector machines (SVM), this research increases network security. The main goal is to increase the accuracy of SVM detection by using a methodical feature selection and optimization technique that is tailored to the complexity of network intrusions. 35 out of the 42 features were chosen for feature selection using RFECV, an algorithmic feature selection technique in machine learning. To ensure that the features preserved are those that contribute most to the model's predictive capacity and that the redundant features are deleted, techniques such as RFECV and feature priority ranking using ExtraTreesClassifier take the model's performance into account during the feature selection process. To improve classifier performance, optimization strategies such as hyperparameter tuning are used, focusing on important data and cutting down on redundancies. The performance of several SVM kernel functions, including linear, polynomial, RBF, and sigmoid, is compared in the study. The Linear model combined with the deep learning model was shown to perform the best. Our model outperforms current IDS frameworks, as demonstrated by comparative analysis, confirming the efficacy of integrating SVMs with deep learning for real-time threat detection. The KDD Cup 99 dataset, which has been widely used as a benchmark for assessing the performance of different IDS models, was used for the work. It offers a consistent, varied, and large dataset so that researchers may evaluate and contrast their methods. Researchers can experiment with feature selection and reduction strategies to enhance IDS performance because of the dataset's broad set of features. © 2024 IEEE.

关键词： Support vector machines

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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