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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Energy Theft Detection in Smart Grids with Genetic Algorithm-Based Feature Selection

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Computers, Materials & Continua 2023年第3期74卷 5431-5446页

作者： Muhammad Umair Zafar Saeed Faisal Saeed Hiba Ishtiaq Muhammad Zubair Hala Abdel Hameed Faculty of Information Technology University of Central PunjabLahore54590Pakistan DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirmingham B47XGUK Faculty of Computer and Information Systems Fayoum University63514Egypt Khaybar Applied College Taibah UniversitySaudi Arabia

As big data,its technologies,and application continue to advance,the Smart Grid(SG)has become one of the most successful pervasive and fixed computing platforms that efficiently uses a data-driven approach and employs efficient information and communication technology(ICT)and cloud *** a result of the complicated architecture of cloud computing,the distinctive working of advanced metering infrastructures(AMI),and the use of sensitive data,it has become challenging tomake the SG *** of the SG are categorized into two main categories,Technical Losses(TLs)and Non-Technical Losses(NTLs).Hardware failure,communication issues,ohmic losses,and energy burnout during transmission and propagation of energy are ***’s are human-induced errors for malicious purposes such as attacking sensitive data and electricity theft,along with tampering with AMI for bill reduction by fraudulent *** research proposes a data-driven methodology based on principles of computational intelligence as well as big data analysis to identify fraudulent customers based on their load *** our proposed methodology,a hybrid Genetic Algorithm and Support Vector Machine(GA-SVM)model has been used to extract the relevant subset of feature data from a large and unsupervised public smart grid project dataset in London,UK,for theft detection.A subset of 26 out of 71 features is obtained with a classification accuracy of 96.6%,compared to studies conducted on small and limited datasets.

关键词： Big data data analysis feature engineering genetic algorithm machine learning

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Evaluation of Supervised Machine Learning Approaches for Credit Card Fraud Detection 14

Evaluation of Supervised Machine Learning Approaches for Cre...

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14th Annual Undergraduate Research Conference on "ICT for Resilient and Sustainable Infrastructure", URC 2022

作者： Balobid, Alhanof Mabruk Binamro, Jawaher Saleh Yohannes, Sewit Tewoldemedhin Kaddoura, Sanaa Zayed University College of Technological Innovation Computing and Applied Technology Department Abu Dhabi United Arab Emirates Zayed University College of Technological Innovation Network and Security Department Abu Dhabi United Arab Emirates Zayed University College of Technological Innovation Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350346800

Credit card fraud is one of the most common attacks affecting millions annually. Detecting fraud transactions using traditional software is not suitable anymore. The attackers are becoming more intelligent in their way of attacking. The investment of machine learning techniques in detecting suspicious activities is the trend nowadays. It helps stop fraud transactions without preventing benign transactions from being completed. This paper applies different supervised machine learning algorithms to a given dataset, such as random forest, gradient boosted trees, logistic regression, k-nearest neighbors, artificial neural network, and others. Consequently, the artificial neural network and the logistic regression algorithms show the highest performance according to the ROC-AUC performance measure. However, the k-nearest neighbor's algorithm performs better according to the F1-score performance measure. © 2022 IEEE.

关键词： Nearest neighbor search

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Blockchain-Based Enhanced Secure Data Storage Model: A Framework for the Internet of Vehicle Networks

Blockchain-Based Enhanced Secure Data Storage Model: A Frame...

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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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Enhanced Intrusion Detection with Advanced Deep Features and Ensemble Classifier Techniques

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SN Computer Science 2025年第4期6卷 1-17页

作者： Toralkar, Pawan Mainalli, Kavita Allagi, Shridhar Debnath, Sanjoy Kumar Bagchi, Susama Leong, Wai Yie Khan, Muhammad Numan Ali Department of Artificial Intelligence Madanapalle Institute of Technology and Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering KLEIT: KLE Institute of Technology Karnataka Hubballi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India INTI International University and Colleges Negeri Sembilan Nilai Malaysia College of Computing and Applied Sciences University of Technology and Applied Sciences Shinas Oman

Intrusion detection is crucial for modern cyber-security, aiming to detect and mitigate unauthorized access and malicious activities within networks. Traditional systems often rely on linear models, which may fail to capture complex patterns in advanced threats. This study proposes a heuristic approach to select the best deep features through an ensemble framework consisting of Random Forest (RF) and Support Vector Machine (SVM) classifiers using soft voting. The novelty of this work is the deep feature extraction through Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder for intrusion detection. These extracted features were fed separately for classification. In order to balance the explainability and accuracy, the ensemble classifier consisting of RF and SVM, uses feature importance ranking (RF) and unambiguous decision bounds (SVM) to provide partial interpretability. Evaluating the NSL-KDD and CICDDoS2019 datasets, our ensemble model with CNN features achieves near-perfect accuracy of 99.99% and 99.91% respectively. RNN and Autoencoder models also show high performance, each reaching 99.3% accuracy on the CICDDoS2019 dataset with Soft Voting classifiers. This work highlights the importance of the ensemble method with better feature selection in intrusion detection. The proposed system outperformed the standard methods by providing a robust, scalable solution for organizational security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution neural network (CNN) Intrusion detection Random forest (RF) Recurrent neural network (RNN) Voting classification (Voting)

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Krishi-Konect: Enhancing Agriculture with Crop Care Solutions 2

Krishi-Konect: Enhancing Agriculture with Crop Care Solution...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Prashant Garg, Tarushi Theodore, Shaji. K.A. Sivakumar, M. SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Kattankulathur Chennai India University of Technology and Applied Sciences College of Computing and Information Sciences Oman

ISBN: (纸本)9798350365269

Agricultural disease detection using convolutional neural networks enhances crop yield by providing early and accurate identification of diseases, mitigating crop losses and boosting productivity. While traditional machine learning techniques are still relevant, deep learning, particularly convolutional neural networks, demonstrates superior performance in image-based disease detection and classification tasks, contingent upon adequate data and computational *** objective of this research is to investigate and evaluate the effectiveness of deep learning techniques, particularly convolutional neural networks (CNNs), in the detection and classification of plant diseases using image-based analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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A lightweight identity based generalized signcryption scheme for secure communication in standard model

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International Journal of Information technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data 15

Comparative Analysis of Differential Privacy Implementations...

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15th IEEE Annual computing and Communication Workshop and Conference, CCWC 2025

作者： Said, Huwida E. Mahmoud, Qusay H. Goyal, Mandeep Hashim, Faiza College of Technological Innovation Zayed University Department of Computing and Applied Technolgy Dubai United Arab Emirates Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONL1G 0C6 Canada College of Information Technology Uae University Department of Computer Science and Software Engineering AI-Ain United Arab Emirates

ISBN: (纸本)9798331507695

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments. © 2025 IEEE.

关键词： Data integrity

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A Novel Congestion Control Scheme Using Fuzzy Logic Systems to Enhance the Path Selection Criteria in Routing Protocols for Low-Power and Lossy Networks on the Internet of Things

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SN Computer Science 2024年第5期5卷 610页

作者： Aimtongkham, Phet Musikawan, Pakarat Kongsorot, Yanika So-In, Chakchai Applied Network Technology (ANT) Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand Advanced Intelligent Interdisciplinary Integration (AIII) Department of Computer Science College of Computing Khon Kaen University Khon Kaen 40002 Thailand

In routing protocols for low-power and lossy (RPL)-based Internet of Things (IoT) networks, congestion control is essential for ensuring efficient and dependable communications with energy awareness. As the number of devices and the amount of data traffic increase, congestion typically occurs, resulting in degraded network performance, i.e., increased packet losses and delays with decreased energy efficiency. Many parameters relating to congestion behaviors have been considered and dynamically changed, as have the possible transmission paths either within a single destination-oriented directed acyclic graph (DoDAG) or across different DoDAGs. Thus, this research investigates congestion mitigation methods for RPL-IoT networks based on a fuzzy logic system (FLS) by proposing a novel FLS scheme to determine the selection criteria for conducting path management in three FLS components: (1) inside/outside the DoDAG selection process, (2) inside the path selection process within a single DoDAG (the so-called inside DoDAG) for high or to-be-high loads, and (3) inside the path selection process across the DoDAGs (the so-called outside DoDAG) during severe congestion applying the concept of a multi-DoDAG node (MDN) to determine both a suitable MDN and data traffic for alternate path selection. Simulation results demonstrate that the proposed method effectively controls congestion levels, decreases packet losses and latency, and attains improved energy efficiency in comparison with the state-of-the-art congestion control schemes in RPL-IoT networks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Congestion controls Fuzzy logic systems Internet of Things Objective function Path selection Routing protocol for low-power and lossy networks

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis 2

Empowering Precision Machine: Deep Learning Technique for Lu...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Jamil, Danish Shah, Syed Mehr Ali Khan, Muhammad Numan Ali Aljarwani, Fatma Mahfoodh Mohammed University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331542573

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics. © 2024 IEEE.

关键词： Lung cancer

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