Enhancing Cybersecurity Strategies Through Adaptive Threat Detection and Resilient Network Defense Mechanisms

Abstract

In an era of increasing digital dependence, cyber threats have become more sophisticated and pervasive. Traditional security measures are no longer sufficient to combat advanced cyberattacks, necessitating the development of adaptive threat detection and resilient network defense mechanisms. This paper explores the current state of cybersecurity strategies, focusing on adaptive methods that use artificial intelligence (AI) and machine learning (ML) to identify and respond to emerging threats in real time. The research also evaluates the importance of network resilience and the role of automation in strengthening defense mechanisms. Through a comprehensive literature review and empirical analysis, this paper identifies key challenges and proposes a framework for enhancing cybersecurity resilience through adaptive strategies. The findings suggest that combining AI-based threat detection with automated defense responses can significantly reduce the impact of cyberattacks and improve the overall security posture of organizations.