As the Internet of Things (IoT) continues to grow at an unprecedented pace, so does the challenge of securing these countless nodes within a network against sophisticated cyber threats. Traditional security measures, which often focus on signature-based detection, are proving inadequate against the dynamic and complex nature of modern cyberattacks. In response, researchers are turning towards adaptive bio-inspired strategies for network security. This approach borrows concepts from natural systems, leveraging the adaptability and resilience found in biological populations to enhance security measures across IoT networks. By implementing multi-population anomaly detection systems, the technique aims to identify and counteract anomalies that suggest potential intrusions or malicious behaviors in real-time. This kind of adaptive security is not only about protecting data integrity but also about maintaining the real-time operational capabilities that IoT networks require. For instance, an anomaly detection system inspired by natural selection could dynamically adapt to detect zero-day attacks, which are previously unknown threats that exploit new vulnerabilities. The innovative aspect of bio-inspired models is their ability to evolve, much like biological organisms, to understand and mitigate new threats through self-adjustment and learning. Consequently, these models promise more robust protection for IoT systems, thereby reducing the risk of widespread disruption caused by cyberattacks.