Deep Q-Network for Enhanced Data Privacy and Security of IoT Traffic

Abstract

Data privacy and security of Internet enabled devices has become a major concern of many users and manufacturers. The proliferation of Internet of Things (IoT) devices and the increasing network traffic have heightened the attack surface. Proxy and man-in-the-middle attacks can be used to exploit vulnerability inherent in IoT devices. Due to the limited resources and features of IoT devices, transmitted data might be at risk of traffic interception and possible decryption of encrypted data on the fly using these attacks. In this paper, we discuss a vulnerability in IoT communication and propose an effective approach based on Deep Q-Network (DQN) and Generative Adversarial Network (GAN) for proxy detection. Our main aim is a robust detection mechanism using network connection information. We further propose a use case for distributed machine learning suitable for real-time monitoring and proxy detection.