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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Shankari, S. Uma Mohameed, Haeedir Kulkarni, Megha Aravindh, S. Purushotham, N. Srm Institute of Science and Technology Department of Computer Science and Applications Ramapuram India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Mohan Babu University Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331529246

Now a days, the standard of living in urban areas is advanced by leveraging technologies such as Cyber-Physical Systems (CPS) and Internet of Things (IoT) with physical infrastructures. However, smart cities highly rely on digital ecosystems, which makes them vulnerable to various cyber threats such as Distributed Denial of Service (DDoS) which leads in violations of privacy and threats to safety of citizens. To develop an effective and efficient system for detection of cybersecurity threats, Box plot Sampling Isolation Forest (BSIF) is proposed. Initially, the data was collected from the Botnet Traffic attack in Internet of Things (BoT-IoT) dataset which contains various normal traffic and Botnet traffic data. After that, Label Encoding (LE) was used for preprocessing the data, which assigns numerical labels to each category in the dataset. Furthermore, Recursive Feature Elimination (RFE) technique was utilized for iterative extraction and selection of features based on their weights is performed by Decision Tree (DT). Finally, the proposed BSIF model is used for detection of cybersecurity threats associated with smart cities, by leveraging box plot sampling method. The proposed obtain better results with 99.74% Precision, 99.92% Accuracy, 99.87% Recall and 99.84% F1-score, compared with Smart Attacks Learning Machine Advisor (SALMA) respectively. © 2025 IEEE.

关键词： Bot (Internet)

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P2FLF: Privacy-Preserving Federated Learning Framework Based on Mobile Fog Computing

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第17期17卷 72-81页

作者： Ankayarkanni, B. Pani, Niroj Kumar Anand, M. Malathy, V. Bhupati Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Odisha Sarang India Department of ECE Dr M.G.R Educational and Research Institute Tamil Nadu Chennai India Department of ECE SR University Telangana Warangal India Department of IoT KL Deemed to be University Andhra Pradesh Vaddeswaram Guntur India

Mobile IoT devices provide a lot of data every day, which provides a strong base for machine learning to succeed. However, the stringent privacy demands associated with mobile IoT data pose significant challenges for its implementation in machine learning tasks. In order to address this challenge, we propose privacy-preserving federated learning framework (P2FLF) in a mobile fog computing environment. By employing federated learning, it is possible to bring together numerous dispersed user sets and collectively train models without the need to upload datasets. Federated learning, an approach to distributed machine learning, has garnered significant attention for its ability to enable collaborative model training without the need to share sensitive data. By utilizing fog nodes deployed at the edge of the network, P2FLF ensures that sensitive mobile IoT data remains local and is not transmitted to the central server. The framework integrates privacy-preserving methods, such as differential privacy and encryption, to safeguard the data throughout the learning process. We evaluate the performance and efficacy of P2FLF through experimental simulations and compare it with existing approaches. The results demonstrate that P2FLF strikes a balance between model accuracy and privacy protection while enabling efficient federated learning in mobile IoT environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fog computing

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Gesture-Based Virtual Keyboard: Enhancing Human-computer Interaction Through Hand Tracking

Gesture-Based Virtual Keyboard: Enhancing Human-Computer Int...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Singh Yadav, Ajay Pratap Yadav, Ganesh Kumar Nagar, Sheetal Sharma, Brijesh Kumar Rawat, Daksh Bhakuni, Vihan Singh Uttar Pradesh Greater Noida201310 India JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India IIMT College of Engineering Department of ECE Greater Noida India SRM Institute of Science and Technology Department of Computer Applications Modinagar India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

In this study, an algorithm is developed that utilizes various OpenCV, cvzone and python libraries to provide a virtual keyboard. The program detects hand gestures, specifically the tips of fingers captured through a webcam, to simulate keystrokes corresponding to buttons on the virtual keyboard displayed on the screen, using Hand tracking module provided by Mediapipe. The virtual keyboard consists of keys of 'QWERTY' format and are represented by a graphical element. When the index finger approaches the corresponding key, it is highlighted and when distance between the tips of middle finger and index finger matches the required distance the corresponding keystroke is generated. The program captures video frames continuously from the webcam, identifies hand gestures, and accordingly updates the virtual keyboard's display. Additionally, the program outputs the text generated in a designated area of the screen using pynput library. The implementation uses object-oriented programming principles, with classes defined for buttons and hand detection functionality. © 2024 IEEE.

关键词： Object oriented programming

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Potential Impacts of Online-Based Learning 2.0 and Certification on Employability 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Saini, Dilip Kumar Jang Bahadur Singh, Sumeshwar Negi, Neerja Bhandari, Sonam Kumar, Raj Joshi, Kapil Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Dehradun India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Rapid advancement in technology innovation has seen an expansion in online-based learning programs as a major aspect of numerous organizations’ course offerings around the globe. Online learning in colleges has gotten more interactive, adaptable, collaborative, and participative. It has gained a fundamental part to play later on as a classroom teaching tool and a self-study platform for skill improvement. The reason for this examination is to consider the likely effects of online-based learning and accreditation regarding employability. The term employability implies a set of abilities, understanding, and individual qualities that make a graduate bound to pick up work and prevail in their chosen occupation. This paper has especially investigated the issues of online certification real availability, their impacts on employability, and their overall points of interest and weaknesses. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented in the final stages of this research paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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Addressing Mobility Based RPL Routing for Low Power Lossy Networks in IoV Networks 2

Addressing Mobility Based RPL Routing for Low Power Lossy Ne...

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

作者： Diniesh, V.C. Murugesan, G. Jude, M. Joseph Auxilius Prabhakaran, A. Haritha, N. Jeevanaa, S.M. Karthikraja, A. Kongu Engineering College Department of Electronics and Communication Engineering Erode India Madanapalle Institute of Technology and Science Department of Computer Applications AP Madanapalle India Kongu Engineering College Department of Electronics and Communication Erode India

ISBN: (纸本)9798350328202

The Internet of Vehicles (IoV) has quickly progressed, linking a myriad of devices to facilitate data-driven applications in several fields. Routing Protocol for Low-power and Lossy Networks (RPL) is crucial to this paradigm, guaranteeing efficient and reliable communication in limited IoT and IoV contexts. In RPL there are two common Objective functions to choose the preferred parent namely Objective Function zero (OF0) and Minimum Rank with hysteresis objective function (MRHOF). OF0 uses Hop count as a metric to choose its preferred parent whereas MRHOF uses Expected Transmission Count (ETX) as a metric. The default Objective function is MRHOF and the metric is ETX. The existing protocols deals only with the static environment and it failed to handle the fast mobility. In this paper, we extend the scenario from IoT to IoV (static and mobile node) under random and grid environment with different network metrics like power, average end-to-end delay and Packet Delivery Ratio. Importantly, simulation is carried on 6LOWPAN/RPL stack with existing objective function on ContikiOS. The simulation result shows that MRHOF achieves 91% packet reception, 24.34% less delay compared against OF0 under fast mobility (10 - 50 m/s). © 2024 IEEE.

关键词： Low power electronics

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Security Attacks and Key Challenges in Blockchain Technology: A Survey 4th

Security Attacks and Key Challenges in Blockchain Technology...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Patel, Rima Patel, Dharmendra Department of Computer Science & Engineering Faculty of Technology & Engineering Devang Patel Institute of Advance Technology and Research CHARUSAT Changa India Department of Computer Application Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT Changa India

ISBN: (纸本)9789811998874

A blockchain is decentralized, distributed and public ledger, which is used to record the transaction across the number of clusters of computers. The record cannot be altered without an alternation in subsequent blocks. In the world of IT, the term Blockchain is not obscure. Despite tremendous applications in the field of information technology, finance and other industries, there are some challenges with Blockchain technology that need to be taken into consideration. In this paper, we have discussed the key challenges and issues with Blockchain technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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Big Data Graph Node Importance Using Page Rank 1

Big Data Graph Node Importance Using Page Rank

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Roseline, R. Vasudevan, B. Clementking, A. Rani, S. Sivakumar, T. Srinivasan, C. PG Department of Computer Applications Cuddalore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India Karnataka Bengaluru India Department of BCA/IT Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Improving the efficiency and accuracy of finding important nodes in large networks is the goal of enhancing Big Data graph node significance analysis using PageRank. To deal with the massive and complicated data sets seen in big data settings, it is necessary to modify and enhance the PageRank algorithm. The objective is to provide a more efficient framework that can speed up computation and improve the accuracy of significance ranking in large-scale networks. To control the algorithm's scalability, it is necessary to use state-of-the-art computational methods, such as parallel processing and effective data structures. The expected result is a scalable and reliable system that can quickly and correctly analyze large networks, yielding useful insights for applications like recommendation systems, online search, and social network analysis. The focus is on making PageRank more feasible for use in large data settings while also increasing its computing efficiency and scalability. The Adjacency Matrix of the Graph in a sample of 5 nodes has a value ranging from 15 to 55 in numbers, according to results from synthetic graph data. In another instance of the same dataset, the Initial PageRank Scores range from 0.2-0.15 in numbers, and in yet another instance, the final PageRank Scores for nodes range from 0.177 to 0.242 in numbers. © 2024 IEEE.

关键词： Data accuracy

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Utilizing Neural Networks for Image and Sound Signal Recognition in Autonomous Driving 2

Utilizing Neural Networks for Image and Sound Signal Recogni...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Chaudhary, Akshita Chaudhary, Gaurav Chattopadhyay, Saumitra Verma, Akhilesh Kumar Sagar, Pramod Kumar Srm Institute of Science and Technology Department of Computer Applications UP Modinagar India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Mount Litera Zee School Department of Science & Technology UP Greater Noida India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India

ISBN: (纸本)9798350371055

This research focuses on exploring the use of neural networks for photograph popularity in autonomous driving. Mainly, the research is investigating how semantic segmentation may be progressed with convolution neural networks (CNNs) to discover avenue objects with higher accuracy and quicker reaction. CNNs are skilled on a large-scale dataset of road scenes, employing complex architectures designed to perceive meaningful photo functions from the information. A contrast of outcomes is then made to standard techniques, which include k-nearest associates and random forest, to assess the performance of the CNNs for street scene segmentation. The goal is to offer a progressed expertise of the capabilities of neural networks for self sustaining driving photo popularity and to expand a framework for deploying such structures in the actual world. © 2024 IEEE.

关键词： architectures performance progressed research world

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Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification

Industry 4.0 based Machine Learning Models for Anomalous Pro...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Kumar, Sourabh Chandra, Saroj Kumar Shukla, Ram Narayan Panigrahi, Lipismita Computer Science and Engineering Op Jindal University Center of Excellence in Industry 4.0 Raigarh India Kalinga Institute of Industrial Technology Department of Computer Applications Bhubaneswar India

ISBN: (纸本)9781665492942

Automation has made tremendous changes in the industries. It has been used to automate the manual processes involved in different physical units of the industries. The purpose was to increase the production in the manufacturing. Now, computers are being used in the industries to monitor functionalities of different production units with the help of artificial intelligence and internet of things (IoT). The IoT has revolutionized the industries. It is an interconnected network system of physical units. The core purpose of it to gather and share information among different physical units. The IoT has great impact on the many areas such as business, industry, medicine, the economy, transport, industrial robots and automation systems. IoT with artificial intelligence has wide range of industrial applications. Industry 4.0 is used in the industries where different industrial units are connected over the internet and interacting to make decisions via machine-to-machine communication. It has increased the benefits of industries in terms of production and supply chain management. Manufacturing industry monitors its production units in every 10 milliseconds to capture features of the product that is being produced. The features generated in this process are huge in amount. Critical observation is performed on the generated features to categorize the product as anomalous or good. Product classification is difficult task in the labeled datasets due to human bias in labeling the final product as anomalous or good. In this work, machine learning models is being used to detect and classify faulty product produced by manufacturing industry. Both qualitative and quantitative study will be carried out to compare various machine learning models. © 2023 IEEE.

关键词： Internet of things

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