Biologically-Inspired Artificial Lymphocyte Networks for Adaptive and Scalable Malware Detection Against Zero-Day and Persistent Threats

The detection of malware is still a crucial problem in the field of cybersecurity and new methodologies have shown up recently such like polymorphism, metamorphism and zero-day attack. Traditional detection approaches, including signaturebased and anomaly-based methods, suffer from their inability to overcome the ever-changing threats, high false positive rates and scalability issues. In this paper, to overcome these limitations a biologically inspired malware detection architecture ISIMD-ALNs is proposed that mimics the processes of adaptive immune system. T-cells offer signature-based detection, B-cells offer behavioral anomaly detection while memory cells provide mechanisms for adaptive learning, and affinity maturation controls learning with reinforcement learning for optimal detection thresholds. A distributed collaboration mechanism that supports scalability in identifying APTs is computed. We show that ISIMD-ALNs achieves a high accuracy of 93%, and superior ROC AUC of 0.994, outperforming traditional baseline models like Random Forest, KNearest Neighbors, and Support Vector Machines. With selftolerance mechanisms, the model is successfully able to detect zero-day attacks while keeping false positive rates low. The underlying design of ISIMD-ALNs is biologically inspired, and is one that promotes continuous learning and adaptability to changing prevalences, making it a workable, robust and scalable solution for modern malware detection. This work lays down the foundations of a paradigm shift in adaptive cybersecurity: introducing ISIMD-ALNs which highlight important gaps within traditional approaches, bridging them with what can lead to sophisticated, scalable, and intelligent malware detection systems.