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Overcoming Dataset Imbalances and Computational Challenges in IoT Intrusion Detection: A SMOTE-Enhanced Transformer-based Model 14

Overcoming Dataset Imbalances and Computational Challenges i...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Venkatasubramanian, S. Pradeep Kumar ch, S.K.B. Babu, Bairysetti Prasad Saranathan College of Engineering Trichy India Ramachandra College of Engineering Eluru India Ramachandra College of Engineering Eluru India

ISBN: (纸本)9798331531935

Due to security holes in IoT-enabled networks, the massive adoption of IoT has unleashed a new wave of threats into computer networks. Due to their limited resources, these devices are ill-equipped to handle security measures, making them the most vulnerable part of our computer networks and putting our data and systems at risk. Some have suggested Intrusion Detection systems (IDS) as a means to lessen the impact of intrusions connected to the IoT in an effort to solve this problem. Although IDS are essential for detecting threats, they are not widely used because of their high computing costs and reliance on labelled data. Finding a happy medium between fast detection and excellent accuracy is the objective of this model development effort. In order to accomplish this, to present a feature selection method based on the black-winged kite algorithm (BKA), a groundbreaking swarm intelligence technique that combines the Leader tactic and the Cauchy variation procedure to find the expansive appropriate convergence solution. BKA is based on the migratory and predatory instincts of black-winged kites. The study used few-shot learning techniques by leveraging transfer learning models and specifically using pre-trained architectures for categorisation. It also fine-tuned four pre-trained transformers. To take into account the imbalance in these datasets as a significant component and use the Synthetic Minority Oversampling Technique (SMOTE) to address it. The research team used BoT-IoT, TON-IoT, and CIC-DDoS2019 as benchmark datasets to assess the impact of the chosen features on the suggested model's performance. Metrics for evaluation include F1-score, recall, precision, and accuracy of detection. Evidence from experiments shows that the suggested ensemble model outperforms state-of-the-art AIDS topologies for IoT networks in terms of detection accuracy and efficiency. © 2025 ieee.

关键词： Network intrusion

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 ieee.

关键词： K-means clustering

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Towards a Network Expansion Approach for Reliable Brain-computer Interface 13

Towards a Network Expansion Approach for Reliable Brain-Comp...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Lee, Byeong-Hoo Yin, Kang Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of Dept. of Artificial Intelligence Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Robotic arms are increasingly being used in collaborative environments, requiring an accurate understanding of human intentions to ensure both effectiveness and safety. Electroencephalogram (EEG) signals, which measure brain activity, provide a direct means of communication between humans and robotic systems. However, the inherent variability and instability of EEG signals, along with their diverse distribution, pose significant challenges in data collection and ultimately affect the reliability of EEG-based applications. This study presents an extensible network designed to improve its ability to extract essential features from EEG signals. This strategy focuses on improving performance by increasing network capacity through expansion when learning performance is insufficient. Evaluations were conducted in a pseudo-online format. Results showed that the proposed method outperformed control groups over three sessions and yielded competitive performance, confirming the ability of the network to be calibrated and personalized with data from new sessions. © 2025 ieee.

关键词： Robotic arms

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Neural Network based Vision-Driven Navigation Aid for Individuals with Physical Disabilities 3

Neural Network based Vision-Driven Navigation Aid for Indivi...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Pandurangan, Raji Gowtham, S. Gugan, J. Saveetha Engineering College Department of Ece Chennai India

ISBN: (纸本)9798331509675

This paper presents an innovative approach to assist physically challenged individuals in navigating a computer interface by controlling the mouse cursor using head movement and eye movement. Real-time video frames are sent to a processing unit via IP using a regular mobile phone camera with the DroidCam application. For accurate facial landmark detection, the system uses the MediaPipe FaceMesh library, which utilizes Convolutional Neural Networks for real-time face and eye landmark recognition. The CNN-based model efficiently identifies and maps critical facial points, concentrating on recognizing and monitoring eye movements. The neural network processes video frames to detect eye regions and calculates the center positions of both eyes, which are then mapped to screen dimensions. The mouse pointer on the screen is controlled based on these eye coordinates, enabling precise cursor navigation. Additionally, the system identifies distinct motions of the left and right eyes to perform specific cursor functions, such as clicking, scrolling, and dragging. By integrating neural network-based detection for accurate tracking and mapping, the system ensures high responsiveness and precision. For individuals seeking alternate control due to disabilities, this work offers a hands-free computer interaction method that is affordable, scalable and requires little hardware, providing a reliable and efficient solution for accessibility. © 2025 ieee.

关键词： Eye movements

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Detection, Challenges for Wormhole Attacks and Blackhole Attacks in WSN to Find the Shortest Path of the Node Using the Traditional Algorithm

Detection, Challenges for Wormhole Attacks and Blackhole Att...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramesh, R. Barath Thangaraj, S. John Justin Saveetha School Of Engineering Saveetha Institute of Medical and Technical Sciences Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

These networks are seriously threatened by wormhole and blackhole attacks, which manipulate the routing protocol for malicious paths to disrupt data communication. Classic route optimization algorithms between nodes include Dijkstra's and AODV;these might fall to those types of attacks. The present paper intends to give an overview of the detection, mitigation strategies, and challenges of prevention methods of Wormhole and Blackhole attacks over WSNs. This paper, therefore, is concerned with the impacts of these attacks on network dynamics and provides for a detection and mitigation scheme coupling conventional algorithms with trust-based metrics and secure routing enhancements. Beyond that, one can estimate the efficiency of the proposed methodology in detecting malicious nodes, preserving packet integrity, and making routing hence resilient by simulations in NS3. These results indicate improved network performance with lower latency, higher packet delivery rates, and extended lifetime, thus providing a practical solution for enhancing security in WSNs. © 2025 ieee.

关键词： Wireless sensor networks

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IoT based Agricultural Monitoring and Alert System 4

IoT Based Agricultural Monitoring and Alert System

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priya, V Vishnu Nisanth, G. Dhanushree, D. Sindhu, M. Varshini, T. Preteka, A.T. Kongu Engineering College Department of Computer Science and Engineering Perundurai India Kongu Engineering College Department of Computer Applications Perundurai India Kongu Engineering College Department of Computer Technology Perundurai India

ISBN: (纸本)9798331523923

The goal of this project is to implement an Internet of Things (IoT)-based Agricultural Monitoring & Alert System (AMAS) that will integrate multiple sensors to continuously monitor agricultural parameters, such as soil moisture levels, soil temperature, water levels, irrigation pump operation status, and water quality, to improve precision farming practices. Enabling farmers to make well-informed decisions, maximize resources, and enhance crop output in real time is the primary objective of the AMAS system. The challenges of using IoT for agricultural monitoring, include issues with adoption among farmers, dependable technology, efficient data administration, and connectivity. Small-scale farmers' financial hardship and rural areas' restricted access to technology. Many obstacles stand in the way of the widespread adoption of IoT-based agricultural monitoring systems, such as smallscale farmers' limited financial resources, rural communities' restricted access to technology, and problems with connectivity, dependable data handling, and system scalability. Machine learning-based prediction models and IoT-integrated smart irrigation are two recent approaches that have showed promise in tackling these issues, but they still face obstacles such high implementation costs, technical complexity, and infrastructure dependence. By improving yields, increasing output, and facilitating remote monitoring, the AMAS system seeks to overcome these limits while taking into account the unique requirements and limitations of farmers. This study plan to enhance productivity, optimize yield, and implement remote monitoring. © 2025 ieee.

关键词： Soil moisture

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Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins 17

Towards Secure AI-driven Industrial Metaverse with NFT Digit...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Prakash, Ravi Thomas, Tony Kerala University of Digital Sciences Innovation and Technology School of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331531195

The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the security of NFT-DTs. Existing NFT clone detection methods often rely on static information like metadata and images, which can be easily manipulated. To address these limitations, we propose a novel deep-learning-based solution as a combination of an autoencoder and RNN-based classifier. This solution enables real-time pattern recognition to detect fake NFT-DTs. Additionally, we introduce the concept of dynamic metadata, providing a more reliable way to verify authenticity through AI-integrated smart contracts. By effectively identifying counterfeit DTs, our system contributes to strengthening the security of NFT-based assets in the metaverse. © 2025 ieee.

关键词： Smart contract

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Transient Stability Analysis of Power System Considering Multiple Inverter Aggregation Model 5

Transient Stability Analysis of Power System Considering Mul...

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5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Yang, Zhennan Liu, Haoyi Xu, Zhanpeng Xu, Qin Electric Power Dispatching and Control Center of Guangdong Power Grid Guangzhou China China Energy Engineering Group Guangdong Electric Power Design Institute Co. Ltd. Guangzhou China

ISBN: (纸本)9798331533694

With the continuous progress of power system and power electronics technology, multiple power electronic devices play an increasingly important role in power systems. Due to the variety and large number of source-side power electronic devices with different operating principles and control strategies, direct modeling and analysis have become extremely complex. Therefore, this paper addressed the problem of simplifying the model by aggregating multiple power electronic devices on the source side and proposes an aggregated model construction technique based on the multi-inverter single-machine equivalence method, which aims to improve the modeling efficiency of the power system, reduce the difficulty of analysis, and optimize the system performance. Through theoretical analysis and experimental verification, it has been proven that the method can effectively simplify the model of source-side power electronic devices under certain conditions and improve the modeling accuracy and analysis efficiency to a certain extent. © 2025 ieee.

关键词： Transient stability

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Imagined Speech State Classification for Robust Brain-computer Interface 13

Imagined Speech State Classification for Robust Brain-Comput...

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13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Ko, Byung-Kwan Kim, Jun-Young Lee, Seo-Hyun Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

This study examines the effectiveness of traditional machine learning classifiers versus deep learning models for detecting the imagined speech using electroencephalogram data. Specifically, we evaluated conventional machine learning techniques such as CSP-SVM and LDA-SVM classifiers alongside deep learning architectures such as EEGNet, ShallowConvNet, and DeepConvNet. Machine learning classifiers exhibited significantly lower precision and recall, indicating limited feature extraction capabilities and poor generalization between imagined speech and idle states. In contrast, deep learning models, particularly EEGNet, achieved the highest accuracy of 0.7080 and an F1 score of 0.6718, demonstrating their enhanced ability in automatic feature extraction and representation learning, essential for capturing complex neurophysiological patterns. These findings highlight the limitations of conventional machine learning approaches in brain-computer interface (BCI) applications and advocate for adopting deep learning methodologies to achieve more precise and reliable classification of detecting imagined speech. This foundational research contributes to the development of imagined speech-based BCI systems. © 2025 ieee.

关键词： Contrastive Learning

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3D-Printed Stainless Steel Electrodes for Advancing Mems Microbial Fuel Cells Toward Sustainable on-chip Energy 38

3D-Printed Stainless Steel Electrodes for Advancing Mems Mic...

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38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Elhadad, Anwar Li, Guangfa Yang, Jiaqi Liu, Dehao Choi, Seokheun State University of New York at Binghamton Integrated Computational Materials Design Lab Department of Mechanical Engineering BinghamtonNY United States State University of New York at Binghamton Bioelectronics & Microsystems Laboratory Department of Electrical & Computer Engineering BinghamtonNY United States State University of New York at Binghamton Center for Research in Advanced Sensing Technologies and Environmental Sustainability BinghamtonNY United States

ISBN: (纸本)9798331508890

This study introduces a stainless steel microbial anode fabricated via advanced laser powder bed fusion, marking a transformative step in microbial fuel cell (MFC) technology. By leveraging this cutting-edge metal 3D printing approach, we achieve a 3D microporous structure with superior electrical conductivity, outstanding corrosion resistance in aquatic environments, robust mechanical strength, and adaptability for surface modification - essential attributes for sustainable, high-performance MFCs. The assembled MFC demonstrates unparalleled power output, along with exceptional durability and stability over multiple cycles, outperforming existing MFCs based on alternative 3D-printed substrates. This advancement not only solidifies the role of MFCs as a sustainable energy solution for powering on-chip electronics but also opens new avenues for scalable, cost-effective bioelectronic systems that align with the demands of next-generation technology. © 2025 ieee.

关键词： Corrosion resistance

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