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Securing IoT-Edge Networks: Federated Deep Learning for Botnet Detection 7

Securing IoT-Edge Networks: Federated Deep Learning for Botn...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Nagasundaram, S. Sindhuja, R. Rajesh Kanna, B. Rajalakshmi, S. Shobana, G. Srivastava, Aviral Vels Institute of Science Technology and Advanced Studies Department of Computer Application Chennai Pallavaram India Saveetha Engineering College Department of Medical Electronics India Alagappa Chettiar College of Engineering and Technology Dept. of Electronics and Communication Engineering Karaikudi India RMK College of Engineering and Technology Department of Electronics and Communication Engineering India College of Science and Humanities Department of Computer Applications SRM Institute of Science and Technology Kattankulathur India Amity University Department of CSE Rajasthan Jaipur300202 India

ISBN: (纸本)9798350340600

Governments throughout the world are encouraging the use of 'smart city' technologies to improve urban residents' day-to-day experiences. In order to improve services like healthcare, electricity distribution, water purification, traffic management, etc., smart cities implement internet-connected technologies. The proliferation of connected gadgets has led to an increase in botnet assaults based on the IoT. The term IoT is used to describe a system of computers that are linked together that may be used for a wide variety of tasks, from environmental monitoring to on-demand power switching and beyond. Many Internets of Things gadgets are inherently disparate, update at irregular intervals, and hide in plain sight on a private or company network. The safety and confidentiality issues surrounding the Internet of Things that need to be addressed in both academic and practical settings. This research study proposes a federated-based solution to botnet attack detection utilizing on-device decentralized traffic data and a deep learning (DL) model. The proposed federated method addresses privacy concerns by preventing data from leaving the network's edge on the device. Instead, the edge layer is used to do the DL calculation, which has the extra benefit of being closer to the source of the data. Many tests are run on newly made public test data sets for deep learning models. Additionally, the sets of data are presented for examination and understanding. The recommended DL model achieved better results than the ML models. Finally, this research shows that the suggested model can detect anomalies with a precision of up to 98%. © 2023 IEEE.

关键词： Internet of things

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Prediction of Cardiac Arrest Using Support Vector Machine and Rough Set 9

Prediction of Cardiac Arrest Using Support Vector Machine an...

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9th International Conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Nayak, Subrata Kumar Pradhan, Sateesh Kumar Mishra, Sujogya Pradhan, Sipali Pattnaik, P.K. Sishu Ananta Mahavidyalaya Department. of Computer Science Gvhss Balipatna Odisha India Utkal University Department of Computer Science and Application Odisha Bhubaneswar India Odisha University of Technology and Research Department of Mathematics Odisha Bhubaneswar India Rbvrr Women's College Dept. of Computer Science and Mathematics Hyderabad India

ISBN: (纸本)9789380544441

Cardiac-related problems have taken millions of lives within a small-time interval around the globe. Cardiac arrest does not have any initial symptoms, always a challenge for the physician to overcome this problem. We have developed a novel technique that includes three concepts, i.e., Rough Set Theory (RST), Support Vector Machines (SVM), and Differential Equation Modeling (DEM). Applying RST, we find that Cardiac arrest is the most significant heart-related disease, which leads to a maximum number of deaths in our state Odisha. We have used two fundamental concepts: SVM and DEM, for predicting cardiac arrest accurately. This study is useful for future prediction of cardiac arrest and also helpful in finding the significant heart-related diseases with high mortality rates. The first phase of our analysis deals with raw heart-related medical data applying RST on these data, and we find that cardiac arrest is the most significant heart-related ailment with a high mortality rate. The subsequent section of the paper deals with the symptoms and future prediction of cardiac arrest based on SVM and DME. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Support vector machines

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Next Generation HR: Transforming Talent Management with AI and ML Innovations

Next Generation HR: Transforming Talent Management with AI a...

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Disruptive Technologies (ICDT), International Conference on

作者： Niharika Singh Pooja Singh Sudeshna Chakraborty Dept. of Master of Computer Application GL BAJAJ Institute of Technology & Management Greater Noida U.P. Department of CSE School of Computing Science & Engineering Galgotias University Greater Noida (U.P.) Department of Computer Application School of Computing Science and Engineering Galgotias University Greater Noida

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-dept. analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success.

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TB Chest X-Ray Diagnostic Technique Using Deep Learning

TB Chest X-Ray Diagnostic Technique Using Deep Learning

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Delhi Section Conference (DELCON), IEEE

作者： Manish Kumar Goyal Dhananjay Kumar Yadav Khushmeen Kaur Brar Manisha Mittal Ananta Ojha Laith H. Alzubaidi Ashish Pathani Dept. of Computer Science & Application Vivekananda Global University Jaipur India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Dept. of Electronics and Communication Engineering Chandigarh University Mohali India Guru Tegh Bahadur Institute of Technology New Delhi India Dept. of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India The Islamic University Najaf Iraq Department of Civil Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

The bacteria Mycobacterium tuberculosis is responsible for the infectious illness tuberculosis (TB). Although the lungs are the primary organs affected, the kidneys, bones, and brain may also be affected. Whenever an infected person coughs, sneezes, or talks, small droplets comprising the bacteria are released into the air and spread through the airborne system. The elimination of tuberculosis (TB) remains a problem due to factors such medication resistance, co-infection with HIV, and scarce resources in high-burden configurations, even with substantial efforts to treat the disease. As a major worldwide health concern, tuberculosis (TB) necessitates quick and precise diagnosis in order to be effectively managed. This study aimed to develop a TB detection model using chest X-ray images obtained from ***, utilizing Google's Collaboration Platform. The model was trained to recognize patterns within the TB chest X-rays to efficiently recognize TB within patients in order to be treated in time. We introduce a technique for transfer learning that proves preferable to the traditional methodology of adopting ImageN et weights. The hybrid methodology utilizes best first search technique in fusion with ResNet 50 model. The SVM classifier is used for categorizing the classes. An accuracy of 93.91 % is attained.

关键词： Support vector machines Tuberculosis Lungs Transfer learning X-rays Bacteria Pattern recognition X-ray imaging Medical diagnostic imaging Kidney

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An Effective Prediction of Rainfall Using Machine Learning Technique

An Effective Prediction of Rainfall Using Machine Learning T...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Nisha Rana Guru Prasad M S Ashutosh Sharma Pratap M S M Anand Kumar Aditya Pai H Dept. of Computer Science and Engineering Doon Institute of Engineering And Technology (DIET) Rishikesh India Dept. Computer Science and Engineering Graphic Era Deemed University Dehradun India Dept. of Applied Science (Chemistry) Doon Institute of Engineering And Technology (DIET) Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Graphic Era (Deemed to be University) Dehradun India Dept. of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting.

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Leveraging UAV technology for Enhanced Disaster Management: Innovations and applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Govind Murari Upadhyay Prashant Vats Surabhi Shanker Saloni Bhushan Sonu Mittal Manju Mandot Dept. of Computer Application Manipal University Jaipur Jaipur Rajasthan India Dept. of CSE School of CSE Manipal University Jaipur Jaipur Rajasthan India Dept. of CSE School of Engineering & Technology K.R. Mangalam University Sohna Road Gurugram Haryana India Dept. of Computer Science/IT Chhatrapati Shivaji Maharaj University Panvel Maharashtra India Dept. of CSE Amity Institute of Information Technology Amity University Noida Uttar Pradesh India Dept. of Computer Science/IT J. R. N. Rajasthan Vidyapeeth University Udaipur Rajasthan India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field.

关键词： Technological innovation Signal processing algorithms Disaster management Transforms Medical services Signal processing Autonomous aerial vehicles Regulation Real-time systems Relays

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Utilizing Anomaly Detection Methods for Identifying Fraudulent Activities in Credit Card Transactions

Utilizing Anomaly Detection Methods for Identifying Fraudule...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： N. Siva Rama Krishna Prasad Amit Gangopadhyay R. Prabakaran Nilamadhab Mishra D. Shyam Prasad Sneha Kapoor Dept. of CSE Saveetha School of Engineering Chennai India Dept. of ECE Mohan Babu University Tirupati India Dept. of Computer Application Arignar Anna Institute of Science and Technology India Dept. of CSE VIT Bhopal University Bhopal India Dept. of Electronics & Instrumentation Engineering CVR College of Engineering Hyderabad India Dept of CSE Swami Vivekanand Subharti University Meerut India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Anomaly detection involves identifying unusual occurrences that could signal potential issues, such as security breaches, system failures, financial fraud, structural defects, or medical errors. In the context of digital finance, credit card fraud has become a significant concern, with fraudulent transactions often designed to appear legitimate. To address this, we employ the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset during sampling, and use the ARIMA model for time series forecasting to ensure accurate results. This research explores various fraud detection techniques and models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Classifier (SVC), and Decision Trees. We evaluate and compare the performance of these models to determine which offers the highest accuracy in detecting fraud.

关键词： Machine learning algorithms Time series analysis Static VAr compensators Nearest neighbor methods Credit cards Vectors Fraud Decision trees Security Random forests

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Prediction of Cardiac Arrest using Generalized Soft Computing Techniques

Prediction of Cardiac Arrest using Generalized Soft Computin...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Radhanath Hota Sachikant Dash Sujogya Mishra Subhalaxmi Das S Lenka G Pradhan Department of Computer Science Engineering GIET University Gunupur Odisha India Department of Computer Science Engineering Gandhi Institute for Education and Technology Gunupur Odisha India Department of Mathematics Odisha University of Technology and Research Bhubaneswar India Dept. of Computer Science and Engineering Odisha University of Technology and Research Bhubaneswar India Department of Computer Science and Application Utkal University Bhubaneswar India

Cardiac-related diseases always lead to fatal consequences around the globe. This research includes vague data analysis of the data related to cardiac anomalies. This study proposes a method to develop a dimension reduction model using vague data related to cardiac diseases using rough set theory (RST). The overall research is divided into two different sections. The first section of this study proposes analyses of the raw cardiac-related data using RST, and the second section calculates the performance accuracy using Principal Component Analysis (PCA) applied to the universal data set; the performance measures also include a combination of two Universal data sets. The above claims are verified using statistical tests

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FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation

FedERA: Framework for Federated Learning with Diversified Ed...

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Federated Learning Technologies and applications (FLTA), International Conference on

作者： Anupam Borthakur Aditya Kasliwal Asim Manna Dipyan Dewan Debdoot Sheet Centre of Excellence in Artificial Intelligence Indian Institute of Technology Kharagpur West Bengal India Dept. of Data Science and Computer Application Manipal Institute of Technology Manipal Karnataka India Department of Electrical Engineering Indian Institute of Technology Kharagpur West Bengal India

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

Federated Learning (FL) is a distributed machine learning (ML) approach that allows multiple devices to train a model while keeping training data private on edge devices. However, existing FL libraries are incapable of: (i) su pporting a wide range of existing FL algorithms, (ii) handling diverse public as well as custom datasets, (iii) discarding unreliable updates from malicious edge devices, (iv) facilitating edge-device training, and (v) implementing model compression to reduce communication overhead. With the rapid progression in AI, there is an increment in the computation power while training the models on edge devices which results in more carbon emission (CE). However, in most of the existing FL frameworks, there is no module for estimating CE. In this paper, we present FedERA, a modular and fully customizable open-source FL framework aiming to address these issues especially also by offering extensive support on heterogeneous edge devices and by incorporating both standalone and distributed training approaches. The integration of new software modules unforeseen in existing FL frameworks not only amplifies the scope of its usability but also fosters environment-friendly FL. We believe that FedERA will be a valuable tool for researchers and practitioners working in FL.

关键词： Training Federated learning Operating systems Computational modeling Training data Carbon dioxide Real-time systems Software Security Usability

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Exploring OSINT for Modern Day Reconnaissance

Exploring OSINT for Modern Day Reconnaissance

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IEEE International Conference on Engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Ajay S Ram Arya Thalakkat Devika Udayan Aiby A Khader Hari K Vipin George Dept. of Computer Science and Engg. Government Engineering College Thrissur India Dept. of Cyber Forensics School of Technology and Applied Science Kottayam India Dept. of Computer Application Kerala Police Academy Thrissur India Police Computer Lab Kerala Police Academy Thrissur India Faculty Kerala Police Academy Thrissur India

The Internet's growth has connected users globally, forming a vast network of data. As new social media platforms with diverse features emerge, individuals are drawn to sharing their personal data online. This inclination stems from a desire to display social status. However, this openness poses risks, as personal data uploaded to the internet can easily unveil one's online identity. Extracting such data is a straightforward process, as a simple correlation of evidence with an online profile can summarize a user's identity. By combining various sources of information linked to a specific individual, a comprehensive profile can be created. As the digital landscape expands, Open Source Intelligence (OSINT) has become an invaluable tool for gathering information. OSINT involves searching and accessing publicly available data to extract relevant information, analyze patterns, and draw conclusions. It encompasses activities like web scraping, data mining, social media monitoring, and online research. OSINT is crucial for understanding public sentiment, identifying potential threats, conducting background checks, investigating crimes, and performing competitive analysis. This paper investigates the impact of OSINT as a modern day reconnaissance tool. Through case studies and real-world examples, the research demonstrates the effectiveness of OSINT in enhancing situational awareness, supporting decision-making for law enforcement agencies, and using OSINT as a modern-day reconnaissance tool.

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