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Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks 20

Beyond Detection: Leveraging Large Language Models for Cyber...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of Things (DCOSS-IoT)

作者： Diaf, Alaeddine Korba, Abdelaziz Amara Karabadji, Nour Elislem Ghamri-Doudane, Yacine Badji Mokhtar Annaba Univ LRS Annaba Algeria Natl Higher Sch Technol & Engn Annaba Annaba Algeria Lab Technol Syst Energet LTSE E3360100 Annaba Algeria Univ La Rochelle L3I La Rochelle France

ISBN: (纸本)9798350369458;9798350369441

In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

关键词： Security Intrusion Prediction GPT BERT Large Language Models LSTM Internet of Things (IoT)

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Energy-Centric Clustering and Routing Using Seagull Optimization Algorithm in Wireless sensor Networks 3

Energy-Centric Clustering and Routing Using Seagull Optimiza...

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3rd ieee international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Anitha, R. Al-Attabi, Kassem Sathyavani, B. Janardhana, D.R. Dineshkumar, R. B. S. Abdur Rahman Crescent Institute Of Science And Technology Department Of Electronics & Communication Engineering Chennai Vandalur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Dept. Of Cse Bengaluru India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350318609

The Wireless sensor Networks (WSN), the imperative for energy-efficient clustering and routing protocols is indisputable. This protocol, designed to meet this critical demand, integrates advanced optimization techniques, aiming to augment network performance and extend system lifespan. Existing methods, while attempting to strike a balance between energy consumption and network lifetime, often encounter challenges, a refined solution. The proposed energy-centric clustering and routing protocol in WSN leverages the Seagull Optimization Algorithm (SOA). This innovative approach optimizes cluster formation and routing dynamics by giving precedence to nodes with higher residual energy, thereby enhancing overall network efficiency. By systematically addressing existing limitations, the SOA-based protocol attains commendable performance metrics, including energy consumption at 0.1435J, End-to-End Delay of 10.4566 sec, Throughput of 0.97498 Mbps, and Network Lifetime of 19,857 rounds. In a comparative analysis with MFA-SSO, HPSO-CSSO, DECR-GOA, and FMCB-ER, the proposed method emerges as superior, effectively overcoming challenges and optimizing operations in WSN. © 2024 ieee.

关键词： Energy utilization

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Exploring Load Forecasting: Bridging Statistical Methods to Deep Learning Techniques in Real-World Environment 20

Exploring Load Forecasting: Bridging Statistical Methods to ...

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20th Annual international conference on distributed computing in Smart sensor systems and the Internet of Things (DCOSS-IoT)

作者： Makris, Ioannis Moschos, Nikolaos Radoglou-Grammatikis, PaPanagiotis Andriopoulos, Nikolaos Tzanis, Nikolaos Malamaki, Kyriaki-Nefeli Fotopoulou, Maria Kaousias, Konstantinos Tompros, George Lampsidis Sarigiannidis, Panagiotis MetaMind Innovat Kozani Greece Ctr Res & Technol Hellas CERTH Athens Greece Hellen Elect Distribut Network Operator HEDNO Athens Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece K3Y Ltd Sofia Bulgaria

ISBN: (纸本)9798350369458;9798350369441

Load forecasting has a significant impact on energy management and planning, facilitating efficient allocation of resources and grid operations. In this study, a comparative analysis of traditional statistical methods and deep learning techniques is conducted utilizing a real-world dataset from the Ikaria islanded grid. This paper focuses on four different forecasting approaches: Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN). Through the appropriate processing of the data, extensive experimentation took place, aiming to capture the complex and nonlinear patterns of the dataset. The results indicated that LSTM and DNN outperformed both ARIMA and SARIMAX in all three evaluation metrics, achieving 0.13, 0.09, and 2.11%, RMSE, MAE, and MAPE, respectively. As a result, this study validates the superiority of deep learning techniques in real-world islanded grid environments being capable of accurately predicting future load values based on historical data.

关键词： Deep Neural Networks Electricity Demand Prediction Load Forecasting Long Short-term Memory Time Series Analysis

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Energy Efficient RF Harvesting System for Self-Sustaining sensor Networks 3

Energy Efficient RF Harvesting System for Self-Sustaining Se...

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3rd international conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Gupta, Manisha Kumar, Manish Chaturvedi, Abhay Gla University Department of Electronics & Communication Engg. Mathura India

ISBN: (纸本)9798331528201

Advancements in low-power CMOS technology have enabled innovative circuit designs for sensor systems, including implantable and wearable devices, as well as wireless sensor nodes (WSNs). However, the challenge of powering these systems persists due to the slower progress in battery technology, which limits the integration of multiple sensor modules and poses risks such as leakage in medical implants. Energy harvesting has emerged as a promising alternative, utilizing ambient sources like RF signals, wind, solar, mechanical vibrations, and thermal energy to power these devices. This study focuses on RF and piezoelectric vibration-based energy scavenging systems, particularly using MOSFETs in novel circuit designs to harness RF energy. Implemented in a 130 nm CMOS process, these designs demonstrate the practicality and efficiency of RF energy harvesting in creating self-sustaining, energy-efficient sensor systems. The findings contribute significantly to the development of more functional and safer sensor applications across various fields. © 2024 ieee.

关键词： CMOS integrated circuits

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Load Model of Inverter-interfaced distributed Generators With GFM Control 3

Load Model of Inverter-interfaced Distributed Generators Wit...

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3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Ling, Xu Wang, Ying Zhang, Yong Wang, Zihan Zhang, Tianxun Wen, Zhilin State Grid Corp China Cent China Branch Wuhan Peoples R China Beijing Technol & Business Univ Coll Comp & AI Engn Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

This paper proposes a new load model for inverter-interfaced distributed generator with grid-forming control. The model is described by piecewise functions and easily integrates with synthesis load model. Various disturbances and grid impedances are considered to establish the generic range of model parameters. The model's accuracy was validated in various scenarios.

关键词： Load model inverter-interfaced DG grid-forming inverter synthesis load model

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Reliable Data Delivery in Underwater Optical Wireless sensor Networks 43

Reliable Data Delivery in Underwater Optical Wireless Sensor...

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Tian, Yu Wang, Lei Lin, Chi Dai, Haipeng Su, Yu Lu, Bingxian Qin, Zhenquan Guo, Peizheng Dalian Univ Technol Sch Software Technol Dalian 116024 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116621 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210024 Jiangsu Peoples R China

ISBN: (纸本)9798350339864

Underwater Optical Wireless sensor Networks (UOWSNs) are gaining an increasing demand in industrial and commercial applications as they can achieve high-speed communication. However, prior arts concentrate on promoting the performance of UOWSNs, while the reliability issue has not been fully addressed. In this paper, we propose a novel reliable data delivery scheme based on a cluster structure. First, we determine the orientation of each sensor for directional optical communication, which aims to establish reliable next-hop links among sensors. We formalize such an orientation problem into a submodular function maximization problem and propose a greedy method with an approximation ratio guarantee to solve it. Then, a cluster head designation scheme is developed to improve the data delivery success rate while minimizing the number of cluster heads. Finally, extensive simulations are conducted to demonstrate the effectiveness of the proposed scheme. The results reveal that compared with other algorithms, the proposed scheme can ensure a data delivery success rate of over 98.5% while only keeping 45.3% fewer cluster heads. Furthermore, test-bed experiments are carried out to verify the applicability of the proposed scheme in practical applications.

关键词： Optical communication

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Internet of Things (IoT) based Pre-Abnormal Detection for Elderly People 3

Internet of Things (IoT) based Pre-Abnormal Detection for El...

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3rd international conference on Automation, computing and Renewable systems, ICACRS 2024

作者： Ganesh, C.S. Sundar Mahesh, P. Mouleeswaran, S. Munishwaran, V. Srinivasan, R. Department of Electrical and Electronics Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331532420

As the number of senior people and those living alone develops, there is a critical need for the creation of an intelligent home monitoring system. The study suggests the development of such a system using Internet of Things (IoT) technologies and the ThingSpeak platform. Embedded sensors in the system monitor vital indicators, sleep habits, and mobility. The collected data is analyzed to identify any irregularities, and caretakers are swiftly notified in the case of an emergency. Furthermore, real-time data transfer to cloud storage allows family members and caregivers to access information via a user-friendly online application. The system employs several hardware components, including Node MCU, an open-source IoT platform, Max30100, a sensor for measuring pulse oximetry and heart rate, MPU6050, a 6-axis motion tracking device (accelerometer and gyroscope), and DHT11, a temperature sensor. IoT is a network of interconnected devices that can collect and exchange data, and ThingSpeak is a platform for IoT data analytics and cloud storage. The proposed system is referred to as IHMS (Intelligent Home Monitoring System), aimed at real-time monitoring and care for elderly individuals. © 2024 ieee.

关键词： sensor nodes

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Multi-Verse Optimization for Node Localization in Wireless sensor Networks 3

Multi-Verse Optimization for Node Localization in Wireless S...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Pitchaimanickam, B. Muthuvel, P. Rajasekar, R. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Tamilnadu Kattankulathur603203 India Saveetha Engineering College Saveetha Nagar Department of Ece Chennai Thandalam602117 India School of Computing Kalasalingam Academy of Research and Education Department of Cse Tamilnadu Krishnankoil India

ISBN: (纸本)9798350369083

Wireless sensor Networks (WSN) are focused on increasing attention to develop their potential applications in healthcare, agriculture, object tracking, and energy management. sensor nodes are placed in random locations. The information was gathered from the unknown positions of the sensor nodes, and this information may be useless for the applications. The accurate position of the node is required for more sensitive applications of WSN. This work proposes multi-verse optimization as a method for locating unknown nodes. Particle swarm optimization and firefly algorithms are contrasted with the suggested method. The experimental findings demonstrate that, compared to alternative algorithms, the suggested technique yields superior outcomes. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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Research on Storage and Query Optimization Algorithm of distributed Database for Big Data 3

Research on Storage and Query Optimization Algorithm of Dist...

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3rd international conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Liu, Lixia College of Information Engineering Engineering University of PAP Shaanxi Xi'an710086 China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519032

This paper deeply discusses the storage and query optimization algorithm of distributed database for big data. Firstly, the importance of distributed database storage optimization is analyzed, and key technologies such as column storage technology, data backup and consistent hash algorithm are introduced. Then, the distributed database query optimization algorithm is mainly studied, and an innovative algorithm combining semi-join, direct join and hash partition equijoin strategy is proposed, and the query plan is dynamically adjusted by introducing an intelligent prediction model to maximize the query performance. The experimental results show that the algorithm has significantly improved the storage efficiency and query performance, which proves its effectiveness and application prospect in the big data environment. Future research will focus on further improving the adaptability and generalization ability of the algorithm to cope with more complex data scenarios and query requirements. © 2024 ieee.

关键词： Structured Query Language

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A TDMA Algorithm for Dual-Hop Energy Harvesting Wireless sensor Networks 3

A TDMA Algorithm for Dual-Hop Energy Harvesting Wireless Sen...

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3rd international conference on computing and Machine Intelligence (ICMI)

作者： Patterson, Tanner D. Hayek, Robert J. Ammar, Ahmed Ohio Northern Univ ECCS Dept Ada OH 45810 USA Northwestern Univ ECE Dept Evanston IL USA

ISBN: (纸本)9798350372977;9798350372984

In this paper, we propose a time division multiple access (TDMA) algorithm and system for a dual-hop energy harvesting wireless sensor network. We assume that the network is divided into clusters, where each cluster has a cluster head and cluster members. A cluster head collects data from its respective cluster members and then transmits the data in one packet to the base station. The proposed algorithm first synchronizes the network and then enables each node to transmit over its designated time slot. The proposed algorithm allows each node to synchronize not only based on a synchronization packet from the base station, but also based on a data packet from another node that is within its communication range. Moreover, the proposed algorithm synchronizes clusters individually, i.e., if a cluster becomes out-of-sync, the algorithm only synchronizes that cluster while allowing the remaining clusters to continue normal operation. The proposed algorithm works also for single-hop networks, and achieves network synchronization without the need for acknowledgment packets, which reduces the average energy consumption and average delay by approximately 1.36 and 1.46 times, respectively, compared to the most recent single-hop algorithm.

关键词： Time Division Multiple Access (TDMA) wireless sensor networks energy harvesting synchronization dual-hop

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