Adaptive and Privacy-Preserving Security for Federated Learning Using Biological Immune System Principles

Abstract

The increasing frequency and sophistication of cyber threats pose significant challenges to the security of distributed systems handling sensitive data. Federated learning, a decentralized machine learning framework, enables collaborative model training without sharing raw data, offering privacy advantages. However, ensuring the security and resilience of federated learning systems remains a pressing concern due to potential vulnerabilities, such as data poisoning and model inversion attacks. This study aims to enhance the security of federated learning systems for website threat intelligence by leveraging nature-inspired principles from biological immune systems. The objective is to design a robust and adaptive framework that addresses evolving cyber threats while preserving data privacy. A security framework was proposed, inspired by the adaptive and self-defensive mechanisms of biological immune systems. Key components include adaptive anomaly detection, dynamic threat response, and privacy-preserving mechanisms. The system architecture was validated using simulated federated learning environments, where machine learning algorithms and differential privacy techniques were employed to monitor and respond to threats in real time. The proposed system demonstrated effective detection of anomalies such as data poisoning and model inversion attacks, achieving high accuracy and low false-positive rates. The dynamic threat response mechanism mitigated potential risks by isolating compromised nodes and restoring model integrity. Privacy-preserving measures, including differential privacy and secure multi-party computation, ensured that sensitive data remained protected during the training process. The nature-inspired approach provided a robust, adaptive solution for enhancing the security of federated learning systems. By mimicking the immune system’s ability to detect and respond to threats, the proposed framework improves resilience against evolving cyber threats, making it suitable for securing sensitive applications such as website threat intelligence. This study highlights the potential of biological principles in addressing modern cybersecurity challenges while safeguarding data privacy.