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A Hybrid Approach to Intrusion Detection in Wireless Sensor networks using Deep Learning

A Hybrid Approach to Intrusion Detection in Wireless Sensor ...

引用

2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Lakshmi, B. Jain, Upma Venkatesh, J. Rao, K. B. V. Brahma Lakshmi, B. Gulati, Jivantika Deemed to Be University School of Computing Sastra Tiruchirappalli India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Madanapalle Institute of Technology and Science Department of Computer Science and Technology Madanapalle India Department of Law Pune India

ISBN: (纸本)9798350375442

There is a vast array of fields making use of WSN, including healthcare, transportation, combat, surveillance, environment, and more. Their limited energy resources, communication capabilities, and processing power define them. Because of their design and placement in dangerous, unsupervised areas, they are susceptible to assaults. The network's overall security is being enhanced with the introduction of new techniques. The three mainstays of WSN security - intrusion prevention, intrusion detection, and mitigation - are usually implemented together. The Intrusion Detection System (IDS), the second line of defense in a wireless sensor network, receives the algorithms and schemes needed to detect intrusions at various levels from this study. Hierarchical sensor networks, which are the building blocks of this study's sensor network design, include clustering and a tree-like structure for communication with the base station. At each architectural level - the node, the cluster head, and the base station - a hybrid intrusion detection system is deployed. The data modification assault was detected with the use of temporal correlations of sensor signals in the node level intrusion detection system. To identify the discrepancy between the detected value and the threshold value, data anomaly is calculated, updated, and compared. Accuracy and precision are considered while deciding on a threshold value. Handling data with care. The cluster-level intrusion detection system uses the Radial Basis function to identify malicious patterns, malicious flooding assaults, and selfish misbehavior. In order to minimize the number of iterations required for RBF training, the optimization techniques of Self Organizing Map and Particle Swarm Optimization are used. The perfect RBF is taught to identify sinkhole attacks, anomalous behavior, selective forwarding, and self-serving misconduct in base station intrusion detection systems. In order to coordinate the three IDS and identify the assaul

关键词： Sensor nodes

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Using IOT and WSNs Technology for Monitoring an Aquaponic Greenhouse System 4

Using IOT and WSNs Technology for Monitoring an Aquaponic Gr...

引用

4th international Conference on Embedded and distributed systems, EDiS 2024

作者： Touhami, Achouak Bourouis, Amina Benahmed, Tariq Touhami, Sana Touhami, Nawal Benahmed, Khelifa Ali Kafi university center Dept. Mathematics and Computer Science Tindouf Algeria Tahri Mohammed university Dept. Mathematics and Computer Science Bechar Algeria Ahmed Draria university Dept. Electrical Engineering Adrar Algeria

ISBN: (纸本)9798331519872

The increasing population, climate fluctuations, soil erosion, limited water resources, and concerns about food security highlight the need for effective and sustainable approaches to food production. Aquaponics represents a sustainable method that conserves land and water while minimizing its environmental impact. This technique operates as a closed-loop, soil-less farming system, requiring meticulous monitoring, control, and management. Monitoring an aquaponic greenhouse system involves tracking various parameters to maintain the health and productivity of both plants and fish. Our research introduces a design and algorithms for monitoring an aquaponic greenhouse, utilizing wireless sensor networks (WSN), aquaponic systems, and Internet of Things (IoT) technology. © 2024 IEEE.

关键词： Aquaponic greenhouse Closed-loop Fish Internet of things Plant Wireless sensor networks

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Automated Deployment of Single-Board computer Based Grid Measurement and Co-Simulation Equipment 3

Automated Deployment of Single-Board Computer Based Grid Mea...

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3rd international Workshop on Open Source Modelling and Simulation of Energy systems (OSMSES)

作者： Pitz, Manuel Wege, Felix Eiling, Niklas Vogel, Steffen Bareiss, Vincent Monti, Antonello Rhein Westfal TH Aachen Inst Automat Complex Power Syst Aachen Germany OPAL RT Germany GmbH Nurnberg Germany Fraunhofer FIT St Augustin Germany

ISBN: (纸本)9798350384697;9798350384680

The trend towards more data-intensive communication patterns that are required by many applications in the energy domain leads to having to deploy and manage a large number of devices that collect or forward data. This is challenging, because the physical device locations are often inaccessible, users often do not have the required expertise, and configuration inconsistencies can lead to communication disruptions. We propose an open-source automated deployment approach for a customizable Raspberry Pi based hardware and software stack including fleet management for creating large networks of geographically distributed measurement and communication devices capable of being used for a variety of use cases. We show how our approach reduces the amount of interactions required during deployment and helps to manage large amounts of devices by using two example deployments in the energy domain: Firstly, a scalable deployment of distributed measurement devices for gathering insights of the power grid state or of individual grid connected devices. Secondly, Co-Simulation Gateways that connect distributed simulation infrastructure to perform large co-simulations.

关键词： energy systems automation deployment cosimulation open-source

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Urban Change Detection based Convolutional Auto Encoder - Content Loss using High-Resolution Images

Urban Change Detection based Convolutional Auto Encoder - Co...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Geethaprathibha, K. Alabdeli, Haider Mamatha Bai, B.G. Kumar, Bura Vijay Divya, Boya Department of It Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

The Change Detection (CD) in high-resolution images is significant for understanding the surface of land. There have developed numerous Deep Learning (DL) approaches to CD, many of these algorithms failed to predict edges and protect changed area of shape from the multi-spectral images. In this manuscript, the Convolutional Auto encoder - Content Loss (CAE-CL) approach is developed to detect the changes in urban areas. The CL function is used in the CAE, which enhances the performance of CAE to detect the changes of urban areas. Initially, the deep features are extracted by using the initial layers of CAE and then the features are given to final detection layer. This last layer detects the changes in urban areas effectively with high performance. Various evaluation measures considered to evaluate the performance of CAE-CL approach. The CAE-CL technique reached out the accuracy of 99.51%, precision of 99.75%, f1-score of 98.92%, Intersection of Union (IoU) of 98.03% and Kappa Coefficient (KC) of 89.27% while comparing with existing techniques. © 2024 IEEE.

关键词： Change detection

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Research on the Influence of distributed Photovoltaic Grid-Connection on the Operation Characteristics of Distribution Transformer 2

Research on the Influence of Distributed Photovoltaic Grid-C...

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2nd IEEE international Conference on Electrical, Automation and computer Engineering, ICEACE 2024

作者： Lv, Xiangyuan Kang, Zhenning Fan, Peilin Wang, Jiasheng Zhang, Chao Taiyuan030032 China Great Power Science and Technology Stock Co. Ltd Fuzhou362000 China

ISBN: (纸本)9798350368208

When distributed photovoltaic systems are integrated into the distribution network, they alter the load characteristics of traditional distribution networks and introduce the characteristics of bidirectional flow. In order to develop the construction and configuration scheme of distribution transformer in active distribution network, firstly, it is necessary to assess how distributed photovoltaic grid connection affects the operational characteristics of distribution transformers by analyzing its impact mechanism. In order to quantify its influence on distribution transformer, a set of probability-based assessment indexes is proposed. On this basis, based on the probability assessment index, the variation of voltage on the low voltage side is studied in this paper, load rate and loss of distribution transformer under different distributed photovoltaic permeability. © 2024 IEEE.

关键词： Photovoltaics

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments 16

Towards Smarter Maritime Security: Apriori Algorithm Optimiz...

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16th international Conference on Ambient systems, networks and Technologies networks, ANT 2025 / 8th international Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Shahin, Mahtab Rahimpour, Saeed Ghasempouri, Tara Bauk, Sanja Fahringer, Thomas Draheim, Dirk Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems. © 2025 Elsevier B.V.. All rights reserved.

关键词： distributed cloud

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Applications of Large-Scale Quantum Neural networks Based on distributed Quantum Computing 16

Applications of Large-Scale Quantum Neural Networks Based on...

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16th international Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Kan, Yin He, Junyuan Xue, Cheng Anhui University AHU-IAI Ai Joint Laboratory Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Hefei China Institute of Artificial Intelligence Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350390643

This research investigates the limitations of current quantum hardware in fulfilling the computational demands of quantum neural networks. It introduces an optimized circuit computed method leveraging bit splitting within the framework of distributed quantum computing. The research exemplifies this approach by decomposing a 25-qubit variational quantum circuit into several smaller 5-qubit circuits, thereby enabling distributed quantum computations. A hybrid quantum-classical neural network architecture employing this methodology was implemented and evaluated on selected computer vision datasets. The results were promising, affirming the viability of the approach. The successful implementation of this architecture suggests that future distributed quantum computing systems will likely consist of scalable, heterogeneous quantum systems interconnected through an extensive quantum network. This configuration is anticipated to significantly enhance computational power and versatility, expanding the scope of quantum computing applications in real-world scenarios. This advancement not only validates the feasibility of distributed quantum architectures but also underscores their potential to revolutionize various technological domains through enhanced computational capabilities. © 2024 IEEE.

关键词： Quantum electronics

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Ethereum Phishing Detection Using Hyperbolic Neural networks and Temporal Information 22

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

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22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Xu, Rong Zhang, Jun Zhu, Xiaoyue Song, Zhaoxiong Yu, Zhi Inner Mongolia University College of Computer Science Hohhot China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331509712

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness. © 2024 IEEE.

关键词： Phishing

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Design and Implementation of an Intelligent Energy Storage Management System for distributed Energy Resources 24

Design and Implementation of an Intelligent Energy Storage M...

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2024 international symposium on Integrated Circuit Design and Integrated systems, ICDIS 2024

作者： Guo, Qiang Zhang, Xin Wu, Xiaohuan Miao, Yufeng Shen, Jianhao Liu, Feng Wu, Yinglai Yuhang Qunli Complete Electrical Manufacturing Branch Hangzhou Power Equipment Manufacturing Co. Ltd. Hangzhou China Hangzhou Electric Power Equipment Manufacturing Co. Ltd. Hangzhou China College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9798400718229

The increasing integration of distributed Energy Resources (DERs) into modern power grids presents challenges in maintaining energy efficiency, grid stability, and cost-effectiveness. To address these challenges, this study focuses on the design and implementation of an Intelligent Energy Storage Management System (ESMS) for DERs. Leveraging advanced technology, the research aims to optimize the management of energy storage within microgrids comprising solar panels, wind turbines, and battery storage systems. Experiments were conducted in a simulated DER environment to compare the performance of traditional and intelligent ESMS systems under various operational scenarios. The intelligent ESMS demonstrated superior performance, achieving up to an 11.7% increase in energy efficiency for solar generation management and reducing grid fluctuations by 33% to 50% under dynamic energy pricing models. Furthermore, the system showed enhanced adaptability to variable renewable energy inputs, with performance improvements ranging from 8.2% to 33.3%. These findings underscore the importance of intelligent ESMS in promoting more efficient and stable grid operations, highlighting its potential to significantly impact future energy management practices. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： distributed Energy Resources

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CPADP: Novel Processor for Efficient Threat Detection in CPS using RISC and DC-GCNN

CPADP: Novel Processor for Efficient Threat Detection in CPS...

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2024 international Conference on distributed systems, computer networks and Cybersecurity, ICDSCNC 2024

作者： Kumar, K. Sravan Suresh, G. Penchalaiah, Usthulamuri Sundeep Kumar, K. Prasanthi, E. Geethanjali Institute of Science and Technology Department of Ece S. P. S. R. Nellore India Geethanjali Institute of Science and Technology Dept. of Ece S. P. S. R. Nellore India Geethanjali Institute of Science and Technology Dept. of Cse S. P. S. R. Nellore India

ISBN: (纸本)9798350375442

In the realm of cybersecurity for cyber-physical systems (CPS), the need for robust and efficient intrusion detection mechanisms has become increasingly critical. The existing CPS had limited processing capabilities of traditional detection systems in CPS, and incompatibility of existing IT- centric security solutions with CPS environments. The challenges in real-time threat detection and response due to high data volumes. Vulnerabilities arising from interconnected nature of cyber-physical systems. This paper proposes a novel Cyber-Physical Attack Detection Processor (CPADP) based on Reduced Instruction Set Computing (RISC) architecture. The CPADP integrates advanced techniques for real-time threat detection in CPS environments, leveraging a comprehensive approach spanning data collection, preprocessing, Independent Component Analysis (ICA)-based feature extraction, and a Dual Channel Graph Convolutional Neural Network (DC-GCNN) for attack classification. The architecture aims to address the challenges posed by CPS environments, where traditional IT- centric security measures often fall short. By harnessing the capabilities of RISC architecture, the CPADP promises enhanced processing efficiency and scalability, crucial for real-time threat mitigation in diverse CPS applications. © 2024 IEEE.

关键词： Convolutional neural networks

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