Adaptive Algorithms for Enhancing Network Security in IoT Devices

Abstract

The Internet of Things (IoT) has transformed various sectors, but its rapid expansion has also exposed significant security vulnerabilities due to the heterogeneous and resource-constrained nature of IoT devices. Traditional security measures, designed for more robust systems, often fall short in this dynamic environment. This paper explores the application of adaptive algorithms to enhance network security in IoT devices. By leveraging machine learning and real-time analytics, adaptive algorithms enable efficient threat detection, dynamic encryption, and optimized resource allocation, all of which are crucial for protecting IoT networks against evolving cyber threats. The integration of adaptive algorithms with Software-Defined Networking (SDN) further strengthens security by allowing dynamic reconfiguration of network resources and fine-grained access control. This research underscores the necessity of adaptive security measures in maintaining the integrity and confidentiality of IoT systems, highlighting their ability to provide robust protection without compromising device performance. As IoT networks continue to grow in complexity and scale, the adoption of adaptive algorithms will be essential in addressing the unique security challenges they present, ensuring the continued safe operation of connected devices in an increasingly interconnected world.