Adaptive Evolution-Inspired Algorithm for Intrusion Detection in Bioinformatics Systems

Area of Science:

• Bioinformatics
• Cybersecurity
• Artificial Intelligence

Background:

• Bioinformatics systems face growing cybersecurity threats due to integration with cloud, AI, and real-time analytics.
• Sensitive genomic and clinical data are vulnerable to exfiltration, injection attacks, and model poisoning.
• Existing defense mechanisms struggle to adapt to evolving cyber threats in bioinformatics.

Purpose of the Study:

• To present an Evolution-Inspired Cyber Defense Architecture (EICDA) for enhanced cybersecurity in bioinformatics.
• To develop an adaptive and resilient defense mechanism mimicking biological immune and evolutionary processes.
• To protect sensitive genomic and clinical data against sophisticated cyber intrusions.

Main Methods:

• Developed EICDA, integrating a genetic algorithm-based detection core with reinforcement-driven adaptation.
• Utilized evolutionary and biological immune system principles for real-time learning and reconfiguration.
• Validated EICDA on NSL-KDD, CICIDS2017, and a simulated genomic API log environment.

Main Results:

• Achieved 96.2% detection accuracy and a 2.8% false positive rate.
• Demonstrated sub-150ms response latency, outperforming traditional methods like SVM, Random Forest, and CNN.
• Showcased superior adaptability and robustness, including resilience under threat drift.

Conclusions:

• EICDA offers a viable, adaptive defense model for next-generation bioinformatics platforms.
• The fusion of evolutionary intelligence and cybersecurity provides robust protection for critical biomedical infrastructures.
• EICDA represents a novel approach to addressing the unique cybersecurity challenges in bioinformatics.