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double sarsa and double Expected sarsa with Shallow and Deep Learning

Journal of Data Analysis and Information Processing 2016年第4期4卷 159-176页

作者： Michael Ganger Ethan Duryea Wei Hu Computer Science Department Houghton College Houghton NY USA

double Q-learning has been shown to be effective in reinforcement learning scenarios when the reward system is stochastic. We apply the idea of double learning that this algorithm uses to sarsa and Expected sarsa, producing two new algorithms called double sarsa and double Expected sarsa that are shown to be more robust than their single counterparts when rewards are stochastic. We find that these algorithms add a significant amount of stability in the learning process at only a minor computational cost, which leads to higher returns when using an on-policy algorithm. We then use shallow and deep neural networks to approximate the actionvalue, and show that double sarsa and double Expected sarsa are much more stable after convergence and can collect larger rewards than the single versions.

关键词： double sarsa double Expected sarsa Reinforcement Learning Deep Learning

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An Efficient Impersonation Attack Detection Method in Fog Computing

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Computers, Materials & Continua 2021年第7期68卷 267-281页

作者： Jialin Wan Muhammad Waqas Shanshan Tu Syed Mudassir Hussain Ahsan Shah Sadaqat Ur Rehman Muhammad Hanif Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electronics Engineering FICTBalochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Department of Computer Science Namal InstituteMianwali42200Pakistan

Fog computing paradigm extends computing,communication,storage,and network resources to the network’s *** the fog layer is located between cloud and end-users,it can provide more convenience and timely services to ***,in fog computing(FC),attackers can behave as real fog nodes or end-users to provide malicious services in the *** attacker acts as an impersonator to impersonate other legitimate ***,in this work,we present a detection technique to secure the FC ***,we model a physical layer key generation based on wireless channel *** generate the secret keys between the legitimate users and avoid impersonators,we then consider a double sarsa technique to identify the impersonators at the receiver *** compare our proposed double sarsa technique with the other two methods to validate our work,i.e.,sarsa and *** simulation results demonstrate that the method based on double sarsa outperforms sarsa and Q-learning approaches in terms of false alarm rate(FAR),miss detection rate(MDR),and average error rate(AER).

关键词： Fog computing double sarsa attack detection physical layer key security

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