Deep Learning Techniques for Anomaly Detection in IoT Devices: Enhancing Security and Privacy
Keywords:
Internet of Things (IoT), Anomaly Detection, Deep Learning, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Autoencoders, Security.Abstract
The rapid proliferation of Internet of Things (IoT) devices has brought significant benefits to various sectors, including healthcare, manufacturing, and smart homes. However, this surge in connectivity also poses substantial security and privacy challenges, primarily due to the diverse nature of IoT devices and their susceptibility to cyber threats. This paper explores the application of deep learning techniques for anomaly detection in IoT devices, aimed at enhancing their security and privacy. We begin by reviewing the unique challenges associated with IoT environments, followed by a detailed examination of various deep learning algorithms, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders, highlighting their effectiveness in identifying anomalies in IoT data streams. Additionally, we present a comprehensive evaluation framework that benchmarks these techniques against traditional anomaly detection methods in terms of accuracy, precision, recall, and computational efficiency. Our findings indicate that deep learning approaches significantly outperform conventional techniques, achieving higher detection rates while minimizing false positives. Furthermore, we discuss the implications of these findings for securing IoT ecosystems and propose recommendations for implementing deep learning-based anomaly detection systems. By advancing the understanding of deep learning applications in IoT security, this study contributes to the ongoing efforts to safeguard privacy and integrity in increasingly connected environments.