A Machine Learning Solution for Securing the Internet of Things Infrastructures

Abstract

Securing Internet of Things (IoT) infrastructures against ever-evolving cyber threats remains a critical challenge in the era of interconnected devices. In this paper, we present a novel machine learning solution for enhancing IoT security through the detection and classification of diverse attacks. Leveraging the NSL-KDD dataset, we applied rigorous data preprocessing procedures, including feature engineering based on the chi-squared test, to select the most informative attributes. Our solution utilizes stacked Long Short-Term Memory (LSTM) networks, capable of capturing temporal dependencies and complex patterns within selected features. By exploiting LSTM's sequential learning and hierarchical representations, our approach effectively classifies attacks, ensuring the integrity and resilience of IoT networks. Comprehensive experiments showcase the superiority of our solution compared to various baseline methods, highlighting its accuracy, precision, recall, and F1-score. The proposed machine learning solution demonstrates remarkable effectiveness in securing IoT infrastructures, paving the way for a safer and more interconnected future.