Xiaozhen Lu | Searcharxiv

arXiv2019

UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming

Cellular systems are vulnerable to jamming attacks, especially smart jammers that choose their jamming policies such as the jamming channel frequencies and power based on the ongoing communication policies and network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular communication framework against jamming. In this scheme, UAVs use reinforcement learning methods to choose the relay policy for mobile users in cellular systems, if the serving base station is heavily jammed. More specifically, we propose a deep reinforcement learning based UAV relay scheme to help cellular systems resist smart jamming without being aware of the jamming model and the network model in the dynamic game based on the previous anti-jamming relay experiences and the observed current network status. This scheme can achieve the optimal performance after enough interactions with the jammer. Simulation results show that this scheme can reduce the bit error rate of the messages and save energy for the cellular system compared with the existing scheme.

eess.SP↗

arXiv2018

IoT Security Techniques Based on Machine Learning

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that need to be addressed to implement these machine learning based security schemes in practical IoT systems.

cs.CR↗