文献类型：期刊文献

英文题名：A Bayesian Q-Learning Game for Dependable Task Offloading Against DDoS Attacks in Sensor Edge Cloud

作者：Liu, Jianhua[1];Wang, Xin[2];Shen, Shigen[1];Yue, Guangxue[3];Yu, Shui[4];Li, Minglu[5]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]SUNY Stony Brook, Dept Elect & Comp Engn, Stony Brook, NY 11790 USA;[3]Jiaxing Univ, Coll Math Phys & Informat Engn, Jiaxing 314001, Peoples R China;[4]Univ Technol Sydney, Sch Comp Sci, Sydney, NSW 2007, Australia;[5]Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China

年份：2021

卷号：8

期号：9

起止页码：7546

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：SCI-EXPANDED(收录号：WOS:000642765500036)、、WOS

基金：This work was supported in part by the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China under Grant LZY21F020001, and in part by the National Natural Science Foundation of China under Grant 61572014 and Grant 61772018.

语种：英文

外文关键词：Bayesian games; distributed denial-of-service (DDoS) attack; edge cloud computing; Q-learning; resource allocation

外文摘要：To enhance dependable resource allocation against increasing distributed denial-of-service (DDoS) attacks, in this article, we investigate interactions between a sensor deviceedgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker's objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.