Machine Learning and Deep Learning Techniques for Cybersecurity Risk Prediction and Anomaly Detection
Mrs. Manasa K, Manasa M, Meghana Urs, Mamatha C
Indexed In: Google scholar
Release Date: 2025 | Copyright:©2025 | Pages: 538
DOI: To be updated
ISBN10: 0 | ISBN13: 0
Machine Learning and Deep Learning Techniques for Cybersecurity Risk Prediction and Anomaly Detection explores cutting-edge methodologies in the realm of cybersecurity. This book delves into the application of machine learning (ML) and deep learning (DL) algorithms to identify and predict potential security threats, ranging from data breaches to system vulnerabilities. By examining various anomaly detection techniques, it provides a comprehensive guide to recognizing abnormal behavior in networks and systems. The book emphasizes practical approaches, using real-world case studies to demonstrate how ML and DL can enhance proactive cybersecurity strategies and ensure robust defense mechanisms in evolving digital environments.
Machine Learning and Deep Learning Techniques for Cybersecurity Risk Prediction and Anomaly Detection provides a comprehensive exploration of the integration of artificial intelligence in enhancing cybersecurity. The book begins with an introduction to the challenges faced in cybersecurity and the pivotal role AI plays in mitigating threats. It then delves into machine learning fundamentals, discussing key algorithms and their practical applications for risk prediction. The text further explores deep learning techniques, focusing on how neural networks can detect advanced cyber threats. A significant portion is dedicated to anomaly detection systems, explaining methods for identifying irregularities in network traffic, user behaviors, and system performance. Additionally, the book covers risk prediction models, utilizing predictive analytics to forecast potential breaches. Through real-world case studies, readers gain insights into the successful deployment of these techniques. The book concludes with a forward-looking perspective, discussing emerging trends and the ongoing challenges in applying AI to cybersecurity. This resource is designed for cybersecurity professionals, researchers, and students, providing valuable knowledge on AI-powered security solutions.
Mrs. Manasa K. is serving as an Assistant Professor in the Department of Computer Science and Applications at SBRR Mahajana First Grade College (Autonomous), Mysuru. She has over seven years of teaching experience. Her research interests include Image Processing and the Internet of Things (IoT). She has published a paper titled “An Intelligent Aspect-Oriented Framework for Testing Mobile Applications” in JETIR, Volume 10, Issue 7, July 2023. She also holds a patent titled “Detection of Security Attack Using Deep Learning in WSN Network.”
Manasa M is a dedicated Assistant Professor of Computer Science with 3 years of experience, specializing in the critical domain of cybersecurity. Her area of interest lies in fortifying digital infrastructures against emerging threats, with a particular focus on Wireless Sensor Networks (WSN). The patent titled 'Detection of Security Attack using Deep Learning in WSN Network' underscores her innovative approach to leveraging deep learning techniques for enhancing security measures. Through her academic and research pursuits, Manasa aims to develop cutting-edge solutions for cybersecurity challenges, mentor students, and contribute meaningfully to the field's advancement.
Meghana Urs is a dedicated Assistant Professor of Computer Science with 3 years of experience in the field. She specializes in computer networks, with a particular emphasis on Wireless Sensor Networks (WSN). Her patented work, 'Detection of Security Attack using Deep Learning in WSN Network', showcases her expertise in applying deep learning techniques to enhance network security. Through her research and academic pursuits, Meghana aims to develop innovative solutions for network security and optimization. She is committed to guiding students, contributing to research in computer networks, and staying updated with the latest advancements in the field.
Mamatha C is a dedicated *Assistant Professor of Computer Science* with *10 years of experience. She specializes in **Artificial Intelligence (AI), with a particular emphasis on **machine learning and intelligent systems*. Her patented work “Real - Time Crop Health Monitoring & Predictive Disease Control Using IOT & AI” show cases her interest in the field of agriculture and how to improve the yield using deep learning concepts. Also published a journal “An Artificial Intelligence with IoT Messaging
Protocol for Precision Farming” which is an add-on towards the modern farming. Her research focuses on applying AI techniques to solve real-world problems, particularly in areas such as data analysis, automation, and intelligent decision-making. Through her academic and research pursuits, *Mamatha C* aims to develop innovative AI-based solutions that contribute to technological advancement and societal development. She is committed to guide students, contributing to research in Artificial Intelligence, and staying updated with the latest innovations and trends in the field of artificial intelligence and ML.