Towards Resilient Cyber Infrastructure: Optimizing Protection Strategies with AI and Machine Learning in Cybersecurity Paradigms

Abstract

Cybersecurity threats are continuously evolving, requiring innovative protection strategies to build resilient cyber infrastructure. Artificial intelligence (AI) and machine learning offer promising capabilities to optimize cyber defenses. This paper provides a comprehensive analysis of how AI and machine learning can be leveraged across cybersecurity paradigms to enhance resilience. First, an overview of the cyber threat landscape highlights increasing sophistication of attacks. Next, core concepts in AI and machine learning are explained. Following this, cybersecurity paradigms including network security, endpoint protection, security analytics, and adversarial AI are examined. For each paradigm, capabilities of AI and machine learning are discussed along with representative use cases. Challenges and limitations are also considered. Finally, a strategic framework is proposed for integrating AI-enabled analytics, automation, and adaption across paradigms to create intelligent, dynamic cyber defenses. Recommendations are provided for developing partnerships between cybersecurity professionals and data scientists to fully realize the potential of AI. This research aims to provide cybersecurity leaders with an in-depth perspective on optimizing protection strategies with AI and machine learning.