Adaptive Evolution-Inspired Algorithm for Intrusion Detection in Bioinformatics Systems

Abstract

The increasing integration of bioinformatics with cloud infrastructure, artificial intelligence, and real-time data analytics has introduced unprecedented cybersecurity challenges. Sensitive genomic and clinical data, when exposed to evolving cyber threats such as data exfiltration, injection attacks, and model poisoning, require more adaptive and resilient defense mechanisms. This research presents an Evolution-Inspired Cyber Defense Architecture (EICDA) that mimics biological immune and evolutionary processes to detect, respond to, and adapt against cyber intrusions targeting bioinformatics-informed systems. The proposed architecture employs a genetic algorithm-based detection core combined with reinforcement-driven adaptation, enabling real-time learning and decision reconfiguration. EICDA is validated on multiple datasets including NSL-KDD, CICIDS2017, and a simulated genomic API log environment, achieving a detection accuracy of 96.2%, a false positive rate of 2.8%, and sub-150 ms response latency. Comparative analyses with SVM, Random Forest, CNN, and traditional AIS highlight EICDA’s superior adaptability and robustness. The framework also demonstrates resilience under threat drift conditions, positioning it as a viable defense model for next-generation bioinformatics platforms. This research provides a novel contribution by fusing evolutionary intelligence with cybersecurity to protect critical biomedical infrastructures.