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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Swedheetha, C. Shruthika, C. Preethi, Rubi Vedha Varshini, T.B.J. Maryam, Sabreen Manikandan, P. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350377972

The next generation of webpages is heavily influenced by large scale, high concurrency, and massive amounts of data. Building data-intensive web apps has made *** popular and effective. We employed scenario testing and benchmark testing to examine and contrast the performance of ***, Python-Web, and PHP. Per the testing results, PHP and Python-Web handle a lot less requests in a given amount of time than ***, which is evident from some useful performance metrics. Our findings, taken together, show that *** is the most lightweight and efficient of the three, making it a good choice for I/O-intensive websites;PHP, on the other hand, is best suited for small- to medium-sized applications, while Python-Web is developer-friendly and excels at large web architectures. This research study includes the performance evaluation of web development technologies. © 2024 IEEE.

关键词： Python

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Protecting SDIIOT Network from Key Breach Attack by using Secret Hash Chain Techniques 2

Protecting SDIIOT Network from Key Breach Attack by using Se...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Arora, Jatin Saluja, Kamal Ghantasala, G S Pradeep Rajesh Sharma, R. Sungheetha, Akey Singh, Susheel Kumar Vidyullatha, Pellakuri Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Heera Lal Yadav Balika Degree College UP Lucknow India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364699

Attacks in a software-defined network (SDN) are hard to find. In light of this, this article offers a versatile modular architecture that enables DDoS attack prevention and mitigation in SDN environments. The interlinked computational devices present in the small items over the communication channel, which allows those devices to transmit industrial data, is known as the Industrial Internet of Things (IIoT). Since the conversation is conducted online, security and privacy breaches are possible. As a result, the authenticated key agreement (AKA) of communicating devices is crucial. However, every IOT device is limited in computational resources, so it might be difficult to add robust privacy and security features. AKA research is now focused on forward secrecy (FS) feasibility that provides control over future keys when a secret key is compromised. IoT devices with limited resources cannot process public key cryptosystems because of intensive calculations. The primary goal of AKA with FS for IIoT is to provide a lightweight security framework. Instead of using a public key security framework, it is using multiple hash values. The security strength of the suggested framework is built on challenge-response mechanisms, one-way hash functions, and symmetric key encryption. The inclusion of this suggested architecture demonstrates that it provides a protection against a variety of attacks, secret key negotiation, anonymity, and unlinkability. The proposed approach is analyzed to assess the performance of the security framework and demonstrates that it is a lightweight mechanism for IIoT. © 2024 IEEE.

关键词： Public key cryptography

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Advanced Malware Detection Methods for Polymorphic Virus Identification 5

Advanced Malware Detection Methods for Polymorphic Virus Ide...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Anil, D. Shreeshayana, R. Kiran, B. Preethi Uvce Bangalore University Department of Computer Science and Engineering Bengaluru India Atme College of Engineering Department of Electrical and Electronics Engineering Mysuru India Pes University Department of Computer Science and Engineering Bengaluru India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (数字)9798350379884

ISBN: (纸本)9798350379884

Polymorphic viruses pose a significant challenge to traditional malware detection methods due to their ability to modify their code structure with each infection, effectively evading signature-based detection. This paper explores advanced malware detection methods tailored for identifying polymorphic viruses, focusing on the integration of machine learning, deep learning and hybrid approaches. Through a comprehensive analysis of static and dynamic features extracted from executables and URLs, this study employs classifiers such as Random Forests and Support Vector Machines (SVMs) to enhance detection accuracy. The SVM classifier demonstrated superior performance, achieving a detection accuracy of 96.23% compared to the 93.67% accuracy of the Random Forest classifier. This research highlights the potential of combining static and dynamic analysis with machine learning techniques to address the limitations of traditional methods, offering a robust framework for detecting and mitigating polymorphic and zero-day threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Pothole Detection based on Machine Learning and Deep Learning Models 3

Pothole Detection based on Machine Learning and Deep Learnin...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Tripathi, Mukesh Kumar Hasini, Sagi Homamalini, Madupally Neelakantappa, M. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India

ISBN: (纸本)9798350348347

Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formation of potholes and cracks on the roads, which can cause damage to vehicles or pose a physical danger to occupants, particularly in underdeveloped countries. Identifying potholes in real-time can help drivers avoid them and prevent accidents. Furthermore, recording their locations and sharing them can assist other drivers and road maintenance organizations take prompt corrective measures. In our attempt to address the issue of pothole detection, The paper aims to combine the latest technological advancements. We aim to develop practical, reliable, adaptable, and modular solutions. To achieve this, we will compare the performance of Random Forest, a machine learning model, with CNN, a deep learning model, in detecting potholes. The experiments were conducted on both models using multiple datasets, and a conclusion was drawn to bring out the benefits of the model with 99% accuracy using CNN and 95% accuracy using Random Forest. © 2023 IEEE.

关键词： Deterioration

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Smart Vehicle Health Monitoring System Based On ARIMA and Ordinal Logistic Regression for Analysis of Vehicle Parameters

Smart Vehicle Health Monitoring System Based On ARIMA and Or...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Mahaalakshmi, B. Padmapriya, R. Ellora Jeya Suji, J. Cloudin, S. Kcg College of Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350315127

Existing Vehicle Monitoring Systems use an OBD-II device to retrieve problem codes and live data from the vehicle, which is then displayed to the user as raw data. A user-friendly system is required so that the user can grasp the vehicle's status on their own, preventing serious accidents. The purpose of this study is to produce a user-friendly and intelligible web application that allows the user to monitor the vehicle's health. The concept of the Internet of Vehicles (IoV) is discussed in this study, in which several vehicles form a network and communicate with one another. Machine learning models (Autoregressive Integrated Moving Average and Ordinal Logistic Regression) are used to estimate future vehicle conditions from available data and categorize the condition as 'Good ', 'Moderate,' or 'Bad'. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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An Optimal Attribute-Focused Privacy-Preserving Anonymization Scheme for Healthcare data in Cloud Systems 4

An Optimal Attribute-Focused Privacy-Preserving Anonymizatio...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Yuvaraj, B. Thumilvannan, S. Josephine, D.C. Jullie Srivenkateswaran, C. Divya, K. Kings Engineering College Department of Computer Science and Engineeirng Chennai India

ISBN: (纸本)9798350300888

Modern healthcare systems rely on the accumulation and analysis of enormous datasets about individual patients, which is where innovative computer technologies such as cloud-based systems come into play. When it comes to protecting the privacy and security of sensitive data stored in cloud services, users face a significant challenge. Protecting people's privacy and securing their personal information is essential. Utilizing the K-means clustering technique, this study presents a novel method for identifying the optimal attribute-focused anonymization algorithm. Here, the Gaussian distribution is applied to eradicate outliers, thereby dramatically improving the quality of anonymized data. Extensive experiments demonstrate that the proposed method reduces information loss and execution time by a factor of 2 and 4, respectively, when compared to current best practices. In addition, its scalability surpasses that of alternative methods for safeguarding the privacy of cloud-stored healthcare information. © 2023 IEEE.

关键词： Sensitive data

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Hybrid Perception Analysis of World Leaders in Reddit using Sentiment Analysis

Hybrid Perception Analysis of World Leaders in Reddit using ...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Sekar, Varun Rishwandh Kannan, Thuhin Khanna Rajesh Suraj, N. Vijay, Priya Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350333817

Natural Language Processing (NLP) is the branch of Artificial Intelligence that deals with the interpretation of human speech. NLP is a vast area of study that is continually being developed each day, with active research happening worldwide. The development of NLP algorithms is of utmost importance to the advancements in the field of Artificial Intelligence. With the increasing popularity of social media and the number of hours spent on social media multiplying, people share their opinion on a wide range of topics and issues. This sudden burst of data generated by social media platforms contains a massive potential when combined with state-of-the-art NLP models, it can be leveraged to our advantage. This work builds a dataset of user comments on the top posts about political leaders on Reddit, using Reddit's API. On Reddit, extensive discussions on various topics occur daily. The end goal of this work is to rank the chosen world leaders based on their likability. To find the general likability of a public personality, we try to classify the comments collected from Reddit using Sentiment Analysis. This paper employs state-of-the-art NLP algorithms such as Flair, DistilBERT, and Text Blob Analysis, and combines the results to get a better final rank of the world leaders. © 2023 IEEE.

关键词： Sentiment analysis

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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

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Alexandria engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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A Novel Approach for Pathology Detection using CNN based Image Registration Techniques

A Novel Approach for Pathology Detection using CNN based Ima...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： SenthilPandi, S. Kalpana, B. Preethi, J. Rahul Chiranjeevi, V. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Information Technology RMD Engineering College Chennai India

ISBN: (纸本)9798350305708

Automatic detection of pathology in images can help in reducing the workload of pathologists and speed up the diagnosis. Advancements in medical imaging technologies have enabled high-quality visualization of tissue structures for anatomical and pathological *** paper is aimed at developing fast and reliable systems for pathology localization towards improving diagnostic accuracy. To develop a robust and accurate image registration algorithm for pathology localization in MRI brain images. A two-stage unsupervised end-end deep learning model called Rigid Transform and B-spline based Convolutional Neural Network (RBCNN) is proposed for registration of MRI images for brain tumor localization. This paper exploits the rigid transform in the first stage, to extract the rigid transform parameters for pre-registration, resulting in a coarse alignment of the images. Then the B-spline transform is used in the second stage to align the image further to obtain the optimal alignment. This paper exhibits promising results with low computation time and robustness compared to state-of-the-art methods. RBCNN can be integrated with existing image segmentation algorithms to segment the tumors. Further, it can be used as a backbone in tumor detection networks. © 2023 IEEE.

关键词： Rigid Transform and B-spline based Convolutional Neural Network (RBCNN) unsupervised end-end deep learning model

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A Bias-Free Revenue-Maximizing Bidding Strategy for data Consumers in Auction-based Federated Learning 33

A Bias-Free Revenue-Maximizing Bidding Strategy for Data Con...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Tang, Xiaoli Yu, Han Li, Zengxiang Li, Xiaoxiao College of Computing and Data Science Nanyang Technological University Singapore ENN Group Beijing China Department of Electrical and Computer Engineering The University of British Columbia Canada

ISBN: (纸本)9781956792041

Auction-based Federated Learning (AFL) is a burgeoning research area. However, existing bidding strategies for AFL data consumers (DCs) primarily focus on maximizing expected accumulated utility, disregarding the more complex goal of revenue maximization. They also only consider winning bids, leading to biased estimates by overlooking information from losing bids. To address these issues, we propose a Bias-free Revenue-maximizing Federated bidding strategy for DCs in AFL (BR-FEDBIDDER). Our theoretical exploration of the relationships between Return on Investment (ROI), bid costs, and utility, and their impact on overall revenue underscores the complexity of maximizing revenue solely by prioritizing ROI enhancement. Leveraging these insights, BR-FEDBIDDER optimizes bid costs with any given ROI constraint. In addition, we incorporate an auxiliary task of winning probability estimation into the framework to achieve bias-free learning by leveraging bid records from historical bid requests, including both winning and losing ones. Extensive experiments on six widely used benchmark datasets show that BR-FEDBIDDER outperforms eight state-of-the-art methods, surpassing the best-performing baseline by 5.66%, 6.08% and 2.44% in terms of the total revenue, ROI, and test accuracy of the resulting FL models, respectively. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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