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

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Rosacea Classification Using Deep Stacking Multi-Layer Model (DSMLM) 2

Rosacea Classification Using Deep Stacking Multi-Layer Model...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Antony Sophia, N. Saravanan, T.R. Vigneshwari, M. Deepika, N. Uma, K. Madhavan, P. Srm Institute of Science & Technology Department of Computational Intelligence Chennai Kattankulathur India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Information Technology Chennai India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Rosacea, a widespread and complex skin disorder affecting most of the world population, is divided into various subtypes with unique clinical features the complexity of these subtypes makes accurate classification difficult. A unique technique called the Deep Stacking Multi-Layer Model (DSMLM) has been proposed as a solution for this complex classification. To extract microelements from clinical images and patient history data, DSMLM uses a multilayered neural network architecture. Using the stacking ensemble strategy to combine predictions from different base models makes it possible to improve the classification performance. The effectiveness of the DSMLM is evaluated using a large dataset of different rose cases, and it shows remarkable accuracy in distinguishing between different rose subtypes © 2024 IEEE.

关键词： Large datasets

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H-BlockEnc: A Pioneering Blockchain Integrated Cryptographic Security Model for IoT-Healthcare Systems 2

H-BlockEnc: A Pioneering Blockchain Integrated Cryptographic...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Jackulin Mahariba, A. Saravanan, T.R. Prabaharan, G. Yempally, Sangeetha Kiruthika, M. Sarveshwaran, Velliangiri Srm Institute of Science and Technology Department of Computational Intelligence TamilNadu Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Hyderabad Narasapur India B v Raju Institute of Technology Department of Electronics and Communication Engineering Hyderabad Narasapur India

ISBN: (纸本)9798350328202

Recent research studies have demonstrated that secured medical data sharing greatly enhances people's quality of life through improving medical treatment and care. The challenges associated with the safety of medical data make it even more difficult for the whole healthcare system to be interoperable. When it comes to striking a balance within the field of healthcare, blockchain technology has begun to emerge as a significant option. It is hard to find well-established solutions, nonetheless, due to the ongoing development of innovative Blockchain technology and the transformation of healthcare facilities. The main purpose of this paper is to develop the most straightforward and efficient security paradigm known as, Healthcare - Blockchain paired with homomorphic encryption (H-BlockEnc) framework for IoT-healthcare systems. Here, cutting-edge blockchain technology has been used to quickly and cheaply secure the safety and confidentiality of medical data. In addition, for increased security and data privacy, the homomorphic encryption standard is also utilized for cryptographic activities. The Beetle Swarm Optimization (BSO) technique is used throughout this process to generate keys as efficiently as possible. Based on processing time, energy consumption, block count, and loss rate, the performance and security level of the suggested H-BlockEnc model are verified and contrasted. © 2024 IEEE.

关键词： Blockchain

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MOALF-UAV-MEC: Adaptive Multi-Objective Optimization for UAV-Assisted Mobile Edge Computing in Dynamic IoT Environments

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IEEE Internet of Things Journal 2025年第12期12卷 20736-20756页

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Al Bayda Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi China School of Software Technology Dalian University of Technology Nanchang Dalian China University of Science and Technology School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge Computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework's efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Adopting GPU computing to support DL-based Earth science applications

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International Journal of Digital Earth 2023年第1期16卷 2660-2680页

作者： Zifu Wang Yun Li Kevin Wang Jacob Cain Mary Salami Daniel Q.Duffy Michael M.Little Chaowei Yang Department of Geography and Geoinformation Science NSF Spatiotemporal Innovation CenterGeorge Mason UniversityFairfaxVAUSA Department of Electrical Engineering and Computer Science University of CaliforniaBerkeleyCAUSA Department of Geography University of California Santa BarbarGoletaCAUSA NASA Goddard Computational and Information Sciences and Technology OfficeGreenbeltMDUSA

With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the past several *** successful in many Earth science areas,Al/DL applications are often challenging for computing *** recent years,Graphics Processing Unit(GPU)devices have been leveraged to speed up Al/DL applications,yet computational performance still poses a major barrier for DL-based Earth science *** address these computational challenges,we selected five existing sample Earth science Al applications,revised the DL-based models/algorithms,and tested the performance of multiple GPU computing platforms to support the *** softwarepackages,performance comparisonsacross different platforms,along with other results,are *** article can help understand how various Al/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing(such as supermicro,GPU clusters,and cloud computing-based)for their Al/ML applications,and to optimize their science applications to better leverage the computing device.

关键词： GPU computing GeoAl open science Earth science artificial intelligence

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Intrusion Detection System in Mobile Cloud Computing Using Bat Optimization Algorithm-Support Vector Machine 2

Intrusion Detection System in Mobile Cloud Computing Using B...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Alabdeli, Haider Yamsani, Nagendar Anitha, D. Chaithra, K.N. Bindu, G. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317558

Mobile Cloud Computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations;hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The Bat Optimization Algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm Optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively. © 2024 IEEE.

关键词： Bat Optimization Algorithm Intrusion Detection System Machine Learning Mobile Cloud Computing Support Vector Machine

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A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems

A Secure Deep Q-Reinforcement Learning Framework for Network...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Bebortta, Sujit Tripathy, Subhranshu Sekhar Sharma, Vandana Behera, Jnana Ranjan Nayak, Aishwarya Ravenshaw University Department of Computer Science Cuttack753003 India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar751024 India Department of Computational Sciences Delhi NCR Campus India Kiit Deemed to Be University School of Electronics Bhubaneswar751024 India Driems University Department of Computer Sceince & Engineering Cuttack74022 India

ISBN: (纸本)9798350373783

IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks © 2024 IEEE.

关键词： Network intrusion

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SMOTE Oversampling and Near Miss Undersampling Based Diabetes Diagnosis from Imbalanced Dataset with XAI Visualization 28

SMOTE Oversampling and Near Miss Undersampling Based Diabete...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Nayan, Nasim Mahmud Islam, Ashraful Islam, Muhammad Usama Ahmed, Eshtiak Hossain, Mohammad Mobarak Alam, Md Zahangir University of Information Technology and Sciences Department of Computer Science and Engineering Bangladesh Independent University Center for Computational and Data Sciences Bangladesh School of Computing and Informatics University of Louisiana at Lafayette United States Independent University Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350300482

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention. © 2023 IEEE.

关键词： Machine learning

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Deep Learning-Based Ensemble Model for Detection of Myocardial Infarction from ECG Data 4th

Deep Learning-Based Ensemble Model for Detection of Myocardi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： D., Saha N., Saha G., Mukherjee A., Chatterjee B., Tudu Department of Computer Science and Engineering Academy of Technology MAKAUT WB Hooghly India Department of Computational Sciences Brainware University WB Kolkata India Department of Printing Engineering Jadavpur University WB Kolkata India Department of Instrumentation Engineering Jadavpur University WB Kolkata India

ISBN: (纸本)9789819726103

The myocardial infarction (MI) is the deadly state of heart attack bearing probable risk of death. The early detection of such state can easily save the human life. The ECG or electrocardiogram can be the good mean of detection of myocardial infarction of any cardiac patient. The interpretation of ECG waveform can be done by the deep learning-based Convolution Neural Network (CNN) model. In the present study, the accuracy of such work has been enhanced by appointing ensemble model consisting of the CNN with Random Forest (RF). The extraction of features from the ECG has been taken place at CNN layer followed by myocardial detection at the RF layer. The PCA has been used to estimate feature reduction followed by encoding of labeled multi-modal data. Signal of ECG data is measured through visibly fluctuation of HRV compared to the adjacent beats. The hyper-parameters have been evaluated before processing of the whole data in CNN resulting into considerable performance gain. The result obtained has got better score over the other conventional classifiers like ANN and SVM with the estimated accuracy value of 98%. Rhythm measurement has also been done through SDNN, RMSSD, and prediction of results were evaluated through the metrics of Precision, Recall, accuracy, and ROC curve in order to detect risk factors in early phase of time with the consequences of reduction in the death rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electrocardiograms

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