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Analysis and study of IDSs for cybersecurity vulnerability detection and prevention: A Comprehensive Review 14

Analysis and study of IDSs for cybersecurity vulnerability d...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shaji, Lijin Suji Pramila, R. Noorul Islam Center for Higher Education Thucklay Computer Applications Tamil Nadu 629180 India Mar Baselios Institute of Technology and Science Computer Science and Engineering Kerala Kothamangalam686693 India

ISBN: (纸本)9798350335095

Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated;thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used. © 2023 IEEE.

关键词： Intrusion detection

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An Effective Analysis of Machine Learning Models for Prediction of Heart Disease 2

An Effective Analysis of Machine Learning Models for Predict...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Patil, Rudragoud Kamkar, Shambahvi C. Goudar, Shwetha Indudhar K.L.S.Gogte Institute of Technology Dept of Computer Science and Engineering Karnataka Belagavi India K.L.S Gogte Institute of Technology Dept.of Master of Computer Applications Karnataka Belagavi India

ISBN: (纸本)9798350372892

Heart failure is a chronic illness that impacts millions of individuals worldwide. In order to forecast heart failure health status early and take the necessary activities to address this global issue an effective machine learning-based technique is required. Globally, heart disease is one of the leading causes of death. Medical professionals find it challenging to forecast since it demands experience and a deeper level of predictive knowledge. The healthcare industry operates in a knowledge-deficient but information-rich environment. Healthcare systems have access to a vast amount of data via the internet, but there aren't many useful tools for analyzing the data to find hidden patterns. Medical efficiency will increase with an automated system, which will also save money and time. This software uses information acquired from Kaggle to forecast the likelihood of a disease developing. To create a prediction model, the following algorithms are used: Random Forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Decision Tree (DT), and Logistic Regression (LR). Phases for feature selection and data preprocessing were finished before the models were created. When the models were evaluated based on accuracy and the F1-score, the RF model performed the best. © 2024 IEEE.

关键词： Adversarial machine learning

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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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Modular irregular labelling in Network Analysis with Complete Bipartite Graph as Auto Proctoring 4

Modular irregular labelling in Network Analysis with Complet...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Sivakumar, S. Vidyanandini, S. Nayak, Soumya Ranjan Aluvala, Srinivas Srm Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai603203 India Srm Institute of Science and Technology Kattankulathur College of Engineering and Technology Department of Mathematics Chennai603203 India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350349856

Modular irregular labeling is an edge irregular labeling of a graph in which the edges and vertices are labelled with 1, 2, 3,.. k such that the weight of all vertices are different. The complete bipartite graph is whose vertices are divided into two subsets such that no edge is in the same subset. In this paper the modular irregular labeling technique is applied in the complete bipartite graph. It is also planned to develop certain theorems in work of applying the modular irregular labeling in complete bipartite graph. An application in complete bipartite graph in the field of networking is also discussed. The aim of the work is to build the theorem and to promote an application in complete bipartite graph. The application is about the autoproctor for the online examination to make work easier for the examiner to examine students. © 2024 IEEE.

关键词： Graph theory

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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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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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Privacy-Preserving Human Activity Recognition in Smart Homes using Deep Learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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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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Multi-Modal Emotion and Gesture Recognition System for Humans and Animals Using computer Vision and Deep Learning 5

Multi-Modal Emotion and Gesture Recognition System for Human...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Nair, Rekha R Babu, Tina Kishore, S. Shivaswamy, Smitha Aruna, S. Bhargavi, Manchikalapudi Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Department of Computer Application Karnataka Mangaluru India Acharya Institute of Technology Department of Computer Applications Bengaluru India School of Science and Computer Studies CMR University Bangalore India Hyderabad India

ISBN: (纸本)9798331518097

Emotion recognition through facial expressions and gesture analysis has emerged as a key area of research in computer vision and human-computer interaction. The ability to automatically detect and classify human emotions has wide-ranging applications, from enhancing user experiences to improving mental health monitoring and surveillance systems. This project builds on this background to develop a comprehensive system for recognizing emotions and gestures in both humans and animals. The system integrates facial emotion recognition, hand gesture interpretation, and animal emotion detection into a single platform. Using advanced deep learning models and computer vision libraries, the system classifies facial expressions into categories such as happiness, sadness, anger, fear, surprise, and neutral. The gesture recognition component interprets hand signals to convey messages like 'need help' or 'need water.' An innovative aspect of this work is the extension of emotion recognition to animals, particularly focusing on species like dogs and monkeys. Implemented primarily in Python, the system leverages libraries such as OpenCV and DeepFace. By combining these different modalities of nonverbal communication analysis, the project presents a versatile solution with potential applications in human-computer interaction, mental health monitoring, security, and animal welfare. © 2024 IEEE.

关键词： Gesture recognition

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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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