Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 11
DOI: https://doi.org/10.59646/819/04
Author: Ms. Sherine Dominick
Abstract
The rapid growth of digital technologies, cloud computing, Internet of Things (IoT) devices, and interconnected networks has significantly increased the complexity and frequency of cyberattacks. Traditional cybersecurity approaches often struggle to detect sophisticated threats, zero-day vulnerabilities, and rapidly evolving attack patterns in real time. Artificial Intelligence (AI) has emerged as a transformative technology in cybersecurity by enabling intelligent threat detection, automated incident response, and predictive security analysis. AI-based cybersecurity solutions utilize machine learning, deep learning, natural language processing, and behavioral analytics to identify malicious activities, detect anomalies, and respond to cyber incidents with greater speed and accuracy than conventional methods. This study explores the practical applications of AI in cybersecurity, focusing on its role in threat detection, malware identification, intrusion detection systems, phishing prevention, and automated security operations. AI-driven systems continuously analyze vast amounts of network traffic, user behavior, and security logs to identify potential threats before they cause significant damage. Furthermore, AI-powered incident response mechanisms help organizations reduce response times, minimize security breaches, and improve overall resilience against cyber threats. The paper also examines the benefits, challenges, and future prospects of AI integration in cybersecurity environments. The findings indicate that AI-based cybersecurity solutions enhance security effectiveness, reduce operational costs, and provide proactive defense capabilities, making them essential components of modern cybersecurity frameworks.
Keywords: Artificial Intelligence (AI), Cybersecurity, Threat Detection, Incident Response, Machine Learning, Deep Learning, Intrusion Detection Systems, Malware Detection, Phishing Prevention, Security Automation, Network Security, Behavioral Analytics.