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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Umar Majigi Richard A. Ikuesan Ismaila Idris Shafi'i Muhammad Abdulhamid Cybersecurity Science Department School of ICT Federal University of Technology Minna Nigeria Department of Computing and Applied Technology College of Technological Innovation Zayed University Information Technology Department Community College of Qatar

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The Internet of Vehicles (IoV) has gained significant attention due to its capability to facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. However, IoV networks are highly susceptible to security and privacy threats, including unauthorized tracking of vehicle locations and Distributed Denial-of-Service (DDoS) attacks. To address these challenges, recent studies have explored blockchain technology as a potential solution for enhancing data security and privacy in IoV networks. This research proposes a blockchain-based enhanced secure data storage model for IoV networks, incorporating lightweight cryptography algorithms to ensure robust security. Additionally, the framework integrates statistical and machine learning models to analyze harvested vehicular data, providing predictive insights for threat detection and mitigation. The study presents a hypothetical V2V communication dataset comprising eleven attributes and four records representing a four-smart-vehicle scenario. Analysis of vehicular speed data reveals an average speed of 55 km/h, with maximum and minimum speeds of 100 km/h and 0 km/h, respectively. The total payload transmitted is 42 KB, with an average payload size of 10.5 KB. A key contribution of this study is the development of a Solana blockchain-based secure data storage model, where transactions are automated and validated through smart contracts. The model efficiently stores the large volume of data generated by smart vehicles and enables real-time data retrieval using embedded internet protocols. The proposed framework is expected to provide a highly secure IoV data storage system upon successful implementation while enhancing threat prediction accuracy and overall network security.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Memory Predictive models Prediction algorithms Data models Blockchains Internet of Vehicles

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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Computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： Computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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A new symbol timing and carrier frequency offset estimation algorithm for OTFS for B5G 2

A new symbol timing and carrier frequency offset estimation ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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Explainable-AI for DoS Attacks Detection in 5G Network Using Deep Learning Models 5

Explainable-AI for DoS Attacks Detection in 5G Network Using...

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Albashayreh, Amjad Tashtoush, Yahya Aldosary, Abdallah Darwish, Omar Albalas, Firas The University of Jordan Department of Computer Science Amman Jordan Jordan University of Science and Technology Department of Computer Science Irbid Jordan Prince Sattam bin Abdulaziz University The Department of Computer Engineering Ar Riyadh Saudi Arabia Eastern Michigan University Information Security and Applied Computing Department YpsilantiMI48197 United States

ISBN: (纸本)9798350354690

With the emergence of the fifth-generation (5G) net-work, numerous revolutionary applications are enabled, including low-latency and machine-type communications. This great in-crease creates a broader security threat concerns, such as denial-of-service (DoS) attacks, which can disrupt network functionality. The complexity and decentralization of 5G networks create new vulnerabilities for adversaries, necessitating comprehensive security procedures to identify, mitigate, and prevent DoS attacks in 5G networks. This paper introduces a novel approach for DoS detection in 5G networks, utilizing deep learning and machine learning models, along with Local Interpretable Model-Agnostic Explanations (LIME), to interpret model predictions and identify the significant role of data features in detecting DoS attacks. The results revealed that the random forest model demonstrated superior recall of 99.98, while BiLSTM demonstrated exceptional performance with a recall of 98.02. © 2024 IEEE.

关键词： 5G mobile communication systems

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Message Verification Protocol Based on Bilinear Pairings and Elliptic Curves for Enhanced Security in Vehicular Ad Hoc Networks

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Computers, Materials & Continua 2024年第10期81卷 1029-1057页

作者： Vincent Omollo Nyangaresi Arkan A.Ghaib Hend Muslim Jasim Zaid Ameen Abduljabbar Junchao Ma Mustafa A.Al Sibahee Abdulla J.Y.Aldarwish Ali Hasan Ali Husam A.Neamah Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennaiTamilnadu602105India Information Technology Department Management Technical CollegeSouthern Technical UniversityBasrah61004Iraq Department of Computer Science College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq College of Big Data and Internet Shenzhen Technology UniversityShenzhen518118China Huazhong University of Science and Technology Shenzhen InstituteShenzhen518000China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen518060China Computer Technology Engineering Department Iraq University CollegeBasrah61004Iraq Department of Mathematics College of Education for Pure SciencesUniversity of BasrahBasrah61004Iraq Institute of Mathematics University of DebrecenDebrecenH-4002Hungary Department of Electrical Engineering and Mechatronics Faculty of EngineeringUniversity of DebrecenDebrecen4028Hungary

Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation ***,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy *** address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed ***,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many *** such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)*** formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably *** proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and ***,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and *** the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security ***,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.

关键词： Attacks bilinear elliptic curve cryptography(ECC) privacy security vehicular ad hoc network(VANET)

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Wasim Haidar SK Wilfred Blessing N. R Sudhakar Kumar Chaubey Zahid Mehmood Kavitha S. J Sutherlin Subitha G College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Aruthenganvilai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission.

关键词： Wireless sensor networks Data analysis Smart cities Decision making Throughput Routing protocols Real-time systems Energy efficiency Delays Predictive maintenance

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga 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 Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

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Machinery Fault Diagnoses in High-Voltage Circuit Breakers Using Empirical Mode Decomposition-Support Vector Machine Model 3

Machinery Fault Diagnoses in High-Voltage Circuit Breakers U...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Al-Farouni, Mohammed Sudhakar, A.V.V. Divya, G. Bala Dhandayuthapani, V. Shivakanth, Gandla Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Electrical And Electronics Engineering Sr University Warangal India Department Of Ece Nitte Meenakshi Institute Of Technology Bengaluru India Department Of It College Of Computing And Information Sciences University Of Technology And Applied Sciences Shinas campus Shinas Oman Department Of Computer Science And Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

ISBN: (纸本)9798350318609

The circuit breakers have a flaw in their trip mechanism. Early mechanical faults like coil-plunger jam are hard to report using traditional fault diagnosis based on the coil current. However, users still have difficulty understanding and trusting the diagnosis results since the standard EMD is opaque in terms of interpretability. In this work, an Empirical Mode Decomposition with Support Vector Machine (EMP-SVM) proposed for machinery fault diagnosis. This work uses the vibration signal recovered from the trip process as an intrinsic time series of the data. At first, The Improved EMD is a data-adaptive multiresolution approach that breaks down signals into physically relevant components based on vibration and coil current signals. Lastly, utilizing the characteristics that were retrieved, the SVM is used to diagnose the defect. The implemented improved EMP-SVM model demonstrates superior performance, achieving Precision of (98.37%), recall of (91.03%) and F1-Score of (96.34%). When compared to previous models like Improved Dempster-Shafer Evidence Theory-Echo State Neural Network (IDSET-ESNN) and Phase Space Reconstruction with Support Vector Machine (PSR-SVM). © 2024 IEEE.

关键词： Empirical mode decomposition

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A Novel U-Net Based CNN Algorithm for High-Accuracy Weather Forecasting 16

A Novel U-Net Based CNN Algorithm for High-Accuracy Weather ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Sivaraman, Arun Kumar Nithiyanantham, Janakiraman Ravindran, Priya Narayanan, S T Sarvesh University of Technology and Applied Sciences Department of Computing and Information Sciences Suhar Oman School of Cse Vellore Institute of Technology Chennai India Digital Engineering Solution Centre Photon Interactive Inc. ON Canada K.L.N. College of Engineering Department of ECE Tamilnadu India School of Computer Science Dalhousie University Halifax Canada

ISBN: (纸本)9798331505264

Accurate weather forecasting is essential for sectors like agriculture, aviation, and disaster management. However, deep learning algorithms face challenges in prediction accuracy due to issues like vanishing gradients, overfitting, and high computational demands. This research proposes a novel U-Net based architecture utilizing a Convolutional Neural Network (CNN) bottleneck layer to improve weather forecasting. Key features include a skip-connection mechanism, modified weight update rules, Gaussian-mutation operations, and the Adam optimizer for enhanced feature extraction and faster, more accurate predictions. The model was tested using precipitation data from Doppler Weather Radar (DWR) Chennai Radar and weather parameters from European Centre for Medium-Range Weather Forecasts (ECMWF). A dedicated GeoServer facilitates realtime data processing. Experimental results show the proposed algorithm achieves 97.5% accuracy, outperforming CNN and long short-term memory (LSTM) models by 5.84% and 2.41%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Toxic Comment Classification Using S-BERT Vectorization and Random Forest Algorithm 1

Toxic Comment Classification Using S-BERT Vectorization and ...

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1st IEEE International Conference on Contemporary computing and Communications, InC4 2023

作者： Kumar, Aparna Ashok Pati, Peeta Basa Deepa, K. Sangeetha, S.Tresa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Karnataka India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Karnataka India University of Technology and Applied Sciences Department of Engineering AlMusannah Oman

ISBN: (纸本)9798350335774

The growing popularity of social media platforms and microblogging websites has led to an increase in the expression of views and opinions. However, conversations and debates on these platforms often lead to the use of toxic comments, which consists of insulting and hateful remarks. To address this issue, it is important for social media systems to be able to recognize harmful comments. With the rising incidence of cyberbullying, it is crucial to study the classification of toxic comments using various algorithms. This study compares the effectiveness of different word and sentence embedding methods, including TF-IDF, InferSent, Bert, and T5 for toxic comments classification. A comparative study is also conducted on the impact of using SMOTE to balance the highly imbalanced dataset. The results of these models are compared and analysed. It is observed that T5 embedding with Random Forest Classifier works best at 0.91 F1-Score. © 2023 IEEE.

关键词： Social networking (online)

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