Hybrid Machine Learning Techniques for Data Encryption, Anomaly Detection, and Zero-Day Attack Prevention
Arokia Suresh Kumar Joseph, Mohan Kumar Gajula, Abufaizur Rahman Abusaih Rahumath Ali, Dr. M. Mahalakshmi
Indexed In: google scholar
Release Date: 17/02/2025 | Copyright:©2025 | Pages: 413
DOI: 10.71443/9788197933608
ISBN10: 8197933608 | ISBN13: 9788197933608
Hybrid machine learning techniques synergistically combine multiple algorithms to enhance data encryption, anomaly detection, and zero-day attack prevention. In data encryption, these techniques utilize deep learning models to analyze and classify encrypted traffic without decryption, preserving privacy while identifying potential threats. For instance, integrating convolutional neural networks (CNNs) with gated recurrent units (GRUs) has demonstrated improved accuracy in detecting anomalies within encrypted network traffic . In anomaly detection, hybrid approaches merge supervised and unsupervised learning to identify deviations from normal behavior, effectively spotting irregularities in consumer networks . Regarding zero-day attack prevention, combining signature-based methods with behavioral analysis enables real-time detection and analysis of previously unknown threats . This multifaceted strategy enhances cybersecurity defenses by leveraging the strengths of various machine learning models to address complex and evolving security challenges.
Hybrid machine learning techniques are increasingly pivotal in enhancing cybersecurity measures, particularly in data encryption, anomaly detection, and zero-day attack prevention. By integrating multiple machine learning models, these hybrid approaches leverage the strengths of each to address complex security challenges more effectively.
In the realm of data encryption, hybrid deep learning models have been developed to detect anomalies within encrypted internet traffic. For instance, combining Convolutional Neural Networks (CNNs) with Gated Recurrent Units (GRUs) has demonstrated superior performance in identifying malicious patterns without decrypting the data, thereby preserving privacy while ensuring security.
For zero-day attack prevention, machine learning models such as Random Forests have been employed to detect previously unknown threats. These models analyze network characteristics to identify anomalies indicative of zero-day exploits, enhancing the ability to mitigate such attacks proactively.
Overall, the fusion of various machine learning techniques in hybrid models offers a robust framework for addressing the multifaceted challenges in cybersecurity, from safeguarding encrypted data to preemptively identifying and neutralizing emerging threats.
Arokia Suresh Kumar Joseph is an accomplished computer science professional with over 18 years of experience in the tech industry. Holding a Master of Computer Applications (MCA) degree from Anna University and a Bachelor of Science (BSc) in Computer Science from Bharathiar University, Arokia has a robust foundation in technology. Currently pursuing his Ph.D. in Cloud Computing, his research focuses on Cloud Security and Dynamic Obfuscation within Cloud CRM environments. Arokia’s professional journey has seen him working in technology management and architecture, with a particular emphasis on cloud-based solutions and security. He currently serves as a Salesforce Manager and Architect at a leading technology company in the United States, overseeing teams that create innovative and cloud-based solutions.
Mohan Kumar G is an Assistant Vice President at Wells Fargo Bank in Charlotte, NC, with 18 years of experience in Information Technology. He specializes in Cybersecurity, Application Resiliency, and Cloud technologies. His work involves creating strategic solutions in cloud environments and cybersecurity, particularly within the financial industry. Mohan has contributed to the banking sector’s technology landscape and has significant expertise in the areas of cloud migration, IT security, and infrastructure resilience.
Abufazlur Rahman Abusalih Rahumath Ali With over 16 years of experience, Abufazlur has a distinguished background in delivering scalable and innovative solutions to clients across various industries. Currently, he works as a Senior Consultant in the Systems Development area, where his work focuses on systems design, development, and implementation. He has a specialized background in Cloud Computing, serving clients globally, with a primary focus on security-oriented solutions. Abufazlur is based in the USA and brings significant expertise in systems development and enterprise technology integration.
Dr. M. Mahalakshmi is an Assistant Professor, Department of Networking and Communications, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai with over 20 years of experience in teaching and research in the fields of Internet of Things, Artificial Intelligence, Computer Vision and Information Security. She has published over 50 research papers in peer reviewed journals and is an expert in Internet of Things, Machine learning, Computer vision and Image processing for various applications. Her research interests include data encryption, anomaly detection, and cyber threat prevention, with a focus on the application of advanced technologies to security and privacy solutions.