Security and Privacy in the Internet of Things: Issues, Challenges, and a Deep Learning-Based Intrusion Detection Framework

Abstract

The Internet of Things (IoT) devices often lack robust defenses, making them easy targets for malware and network attacks. At the same time, pervasive data collection raises privacy concerns such as user profiling and location tracking. In this paper, we examine key IoT security and privacy issues and propose a machine learning-based intrusion detection framework. We design a deep neural network (multilayer perceptron) trained on a synthetic IoT traffic dataset to distinguish normal behavior from attacks. We compare its performance against several baseline classifiers. In our experiments, the proposed IDS achieves 97.8% accuracy (F1 score 96.5%), significantly outperforming traditional methods. This demonstrates the potential of adaptive learning for securing IoT networks. Our contributions include a comprehensive analysis of IoT threats and privacy challenges, a novel IDS design suited for resource-constrained networks, and a simulated evaluation framework. These results provide insights for building more secure, privacy-aware IoT systems.