Mobile Edge Computing Against Smart Attacks with Deep Reinforcement Learning in Cognitive MIMO IoT Systems
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Mobile Edge Computing Against Smart Attacks with Deep Reinforcement Learning in Cognitive MIMO IoT Systems Songyang Ge1 · Beiling Lu2 · Liang Xiao3 · Jie Gong2 · Xiang Chen1
· Yun Liu4
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In wireless Internet of Things (IoT) systems, the multi-input multi-output (MIMO) and cognitive radio (CR) techniques are usually involved into the mobile edge computing (MEC) structure to improve the spectrum efficiency and transmission reliability. However, such a CR based MIMO IoT system will suffer from a variety of smart attacks from wireless environments, even the MEC servers in IoT systems are not secure enough and vulnerable to these attacks. In this paper, we investigate a secure communication problem in a cognitive MIMO IoT system comprising of a primary user (PU), a secondary user (SU), a smart attacker and several MEC servers. The target of our system design is to optimize utility of the SU, including its efficiency and security. The SU will choose an idle MEC server that is not occupied by the PU in the CR scenario, and allocates a proper offloading rate of its computation tasks to the server, by unloading such tasks with proper transmit power. In such a CR IoT system, the attacker will select one type of smart attacks. Then two deep reinforcement learning based resource allocation strategies are proposed to find an optimal policy of maximal utility without channel state information(CSI), one of which is the Dyna architecture and Prioritized sweeping based Edge Server Selection (DPESS) strategy, and the other is the Deep Q-network based Edge Server Selection (DESS) strategy. Specifically, the convergence speed of the DESS scheme is significantly improved due to the trained convolutional neural network (CNN) by utilizing the experience replay technique and stochastic gradient descent (SGD). In addition, the Nash equilibrium and existence conditions of the proposed two schemes are theoretically deduced for the modeled MEC game against smart attacks. Compared with the traditional Q-learning algorithm, the average utility and secrecy capacity of the SU can be improved by the proposed DPESS and DESS schemes. Numerical simulations are also presented to verify the better performance of our proposals in terms of efficiency and security, including the higher convergence speed of the DESS strategy. Keywords Mobile edge computing · Server selection · Smart attack defence · Deep reinforcement learning
1 Introduction With the rapid developments of Internet of Things (IoT), more and more IoT devices such as sensors, cognitive
The work is supported in part of Science, Technology and Innovation Commission of Shenzhen Municipality (No. JCYJ20170816151823313), NSFC (No. U1734209, No. 61501527), States Key Project of Research and Development Plan (No. 2017YFE0121300-6), The 54th Research Institute of China Electronics Technology Group Corporation (No. B0105), Guangdong R&D Project in Key Areas (No.2019B010156004) and Guangdong Provincial Special Fund For Mod
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