<p>The rapid expansion of wireless communication systems and on-demand computing resources over the internet has created new opportunities and challenges in ensuring the accessibility and security of critical infrastructure. Cyberattacks in various forms like distributed denial-of-service (DDoS), resource exploitation or reconnaissance etc. can significantly reduce the accessibility, performance and security and exposing more and more flaws in these technologies, resulting in regular disruptions, monetary losses and reputational harm. This research study introduces an Artificial Immune System (AIS) based Intrusion detection System (IDS) that monitors, detects and mitigates attacks effectively by combining the entropy fluctuation method and a novel Deep Maxout classifier. By analyzing entropy variations in network traffic, the system identifies deviations indicative of potential attacks. An Improved Activation and Loss Function (IALF)-based Deep Maxout classifier and a Deep Belief Network (DBN) are combined in a hybrid model that is highly effective at detecting complex, nonlinear patterns and adjusting activation and loss functions. These insights are then further processed to distinguish malicious activity from normal behavior, resulting in enhanced detection accuracy. Upon detection, the Entropy-based Mitigation Process (EMP) isolates malicious nodes to ensure secure data transmission using normalized correlation coefficients and quartile deviation metrics. The proposed system is evaluated using diverse datasets, which represent a wide range of cyberattacks on computing resources and communication systems. Experimental findings show that the system outperforms traditional models in terms of detection accuracy and adaptability while minimizing false positives. A dynamic mitigation approach addresses both current and future security needs by enabling continuous, adaptive protection against dynamic threat landscape.</p>