AI-Powered Adaptive Security Policies for Dynamic Cloud-Based Communication Networks

The rapid evolution of cloud-based communications networks demands adaptive security that is able to match pace with growing threats and dynamic network patterns. This research puts forward an AI-based system for developing adaptive security rules with the help of machine learning algorithms and real-time data analysis in order to identify, predict, and neutralize risks before they occur. Our finding shows that the AI policy discussed in the paper outperforms classical security approaches on most of the attacking channels with DDoS, SQL injection, Phishing, and zero-day attack detection rates of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0 \%, ~} \mathbf{8 5 \%, ~} \mathbf{8 8 \%}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 0 \%}$</tex>, respectively, and an overall detection rate of 85%. In contrast, conventional security techniques were detected on average just under 65 percent of the time against the same attacks. The experimental results show the potential of AI-based policies in handling changing attack patterns, minimizing human intervention, and ensuring the scalability of the systems. This adaptive AI based policy is a potential solution to secure dynamic cloud communication networks from sophisticated cyber-attacks.