SCALABLE SOFTWARE ARCHITECTURE FOR DYNAMIC THREAT DETECTION AND MITIGATION IN IOT

Abstract

In the landscape of ever-expanding interconnectivity, safeguarding Internet of Things (IoT) environments against intrusions emerges as a paramount concern. This paper introduces an innovative Machine Learning Framework tailored specifically for Intrusion Detection in IoT Environments. The framework integrates a Generative Adversarial Network (GAN) module and strategically leverages the SMOTE (Synthetic Minority Over-sampling Technique) technique. Central to its efficacy is the meticulous curation of datasets and a comprehensive approach encompassing data preprocessing and feature engineering. By augmenting traditional machine learning algorithms with GAN for enhanced data generation and SMOTE for rectifying class imbalance, this framework achieves a notable improvement in intrusion detection accuracy. Empirical evaluations conducted against baseline methods demonstrate the framework's superiority, showcasing significantly elevated accuracy, precision, and recall metrics. This research represents a substantial advancement in fortifying IoT security, offering a robust and adaptable solution to counter an array of intrusion threats, thereby contributing significantly to the resilience of IoT ecosystems.