Adaptive Security Framework for the Internet of Things: Improving Threat Detection and Energy Optimization in Distributed Environments

The increasing use of Internet of Things (IoT) devices in critical sectors has increased exposure to security threats, making protecting these systems a priority challenge. Based on static configurations, traditional security approaches have proven ineffective in the face of the dynamic nature of emerging threats, as they cannot adapt in real time to changes in the environment or new attack vectors. This work proposes an adaptive security framework for Internet of Things (IoT) systems capable of autonomously detecting, mitigating, and adapting to various threats, improving precision and response times, and optimizing energy consumption. The framework was implemented in a distributed Internet of Things environment, using adaptive architectures based on the Robot Operating System (ROS) and microservices orchestration with Kubernetes. The results showed a significant improvement in response time, with a reduction of 44%, reaching an average of 250 milliseconds, compared to 450 milliseconds for static approaches. Furthermore, a 92% precision in threat detection was achieved, reducing false positives to 4% and false negatives to 6%. Power consumption was controlled, reaching a maximum of 160 milliamp-hours after facing multiple threats, confirming the system’s efficiency in resource-limited environments. These results demonstrate that the proposed adaptive framework is a robust and efficient solution for security in Internet of Things environments, overcoming the limitations of traditional approaches and ensuring adequate protection without compromising energy efficiency.