A Bayesian <i>Q</i>-Learning Game for Dependable Task Offloading Against DDoS Attacks in Sensor Edge Cloud

Liu, Jian Hua; Wang, Xin; Shen, Shigen; Yue, Guangxue; Yu, Shui; Li, Minglu

doi:10.1109/jiot.2020.3038554

Cited by 59 publications

(26 citation statements)

References 49 publications

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“…Zhou et al 24 proposed a malware detection model based on game theory in WSNs. Liu et al [25][26][27][28] proposed a series of methods for virtual resource security detection in sensor edge cloud.…”

Section: Related Workmentioning

confidence: 99%

An intrusion detection mechanism for IPv6-based wireless sensor networks

Wei

Rong

Liang

et al. 2022

International Journal of Distributed Sensor Networks

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With the advancement of IPv6 technology, many nodes in wireless sensor networks realize seamless connections with the Internet via IPv6 addresses. Security issues are a significant obstacle to the widespread adoption of IPv6 technology. Resource-constrained IPv6 nodes face dual attacks: local and Internet-based. Moreover, constructing an active cyber defense system for IPv6-based wireless sensor networks is difficult. In this article, we propose a K-nearest neighbor-based intrusion detection mechanism and design a secure network framework. This mechanism trains an intrusion detection algorithm using a feature data set to create a normal profile. The intrusion detection algorithm uses the normal profile to perform real-time detection of network traffic data to achieve rapid detections connecting many devices in a wireless sensor network. In addition, we develop a test platform to verify this mechanism. Experimental results show that this mechanism is appropriate for IPv6-based wireless sensor networks and achieves a low false-positive rate and good intrusion detection accuracy at an acceptable resource cost.

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Section: Related Workmentioning

confidence: 99%

An intrusion detection mechanism for IPv6-based wireless sensor networks

Wei

Rong

Liang

et al. 2022

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…In the previous subsection, we derived the optimality system for seeking optimal solutions to the optimal cloud assistance problem (12), in which a sub-problem (14) needed to be solved.…”

Section: Further Properties Of Optimal Solutionsmentioning

confidence: 99%

“…Theorem 3 shows that if the function h is once continuously differentiable and convex, the component of the optimal control (i.e., u i (t)) may be a binary policy. This property can sometimes accelerate solving the optimal CA policy when u i (t) has a binary structure, because in that case we have no need to use the one-dimensional numerical searching method for solving the sub-problem (14).…”

Section: Theorem 3 If the Function H Is Continuous And Convex Any Local Minimum Of The Function Q Is The Global Minimum Of Q As Well Morementioning

confidence: 99%

“…Theorem 4 shows that if the function h is continuous and concave, any component of the optimal control (i.e., u i (t)) must be a binary policy. This property will greatly accelerate solving the optimal CA policy, because we can simply compare the objective values of u i (t) = 0 and u i (t) = 1 when solving the subproblem (14).…”

Section: Theorem 4 If the Function H Is Continuous And Concave It Follows That: (A) If H(0)mentioning

confidence: 99%

“…Figure 1 shows a diagram of the relevant scenario. Therefore, defending against edge DDoS attacks has become an essential issue in the field of edge computing [13,14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal cloud assistance policy of end-edge-cloud ecosystem for mitigating edge distributed denial of service attacks

2021

J Cloud Comp

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Edge computing has become a fundamental technology for Internet of Things (IoT) applications. To provide reliable services for latency-sensitive applications, edge servers must respond to end devices within the shortest amount of time possible. Edge distributed denial-of-service (DDoS) attacks, which render edge servers unusable by legitimate IoT applications by sending heavy requests from distributed attacking sources, is a threat that leads to severe latency. To protect edge servers from DDoS attacks, a hybrid computing paradigm known as an end-edge-cloud ecosystem provides a possible solution. Cloud assistance is allowed with this architecture. Edge servers can upload their pending tasks onto a cloud center for a workload reduction when encountering a DDoS attack, similar to borrowing resources from the cloud. Nevertheless, before using the ecosystem to mitigate edge DDoS attacks, we must address the core problem that edge servers must decide when and to what extent they should upload tasks to the cloud center. In this study, we focus on the design of optimal cloud assistance policies. First, we propose an edge workload evolution model that describes how the workload of the edge servers change over time with a given cloud assistance policy. On this basis, we quantify the effectiveness of the policy by using the resulting overall latency and formulate an optimal control problem for seeking optimal policies that can minimize such latency. We then provide solutions by deriving the optimality system and discuss some properties of the optimal solutions to accelerate the problem solving. Next, we introduce a numerical iterative algorithm to seek solutions that can satisfy the optimality system. Finally, we provide several illustrative numerical examples. The results show that the optimal policies obtained can effectively mitigate edge DDoS attacks.

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Modeling and simulation of defense game model for jamming attack in wireless sensor networks using evolutionary game theory

K¹

2021

Concurrency and Computation

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The open and shared communication medium of wireless sensor networks creates vulnerability to various types of attacks. In particular, jamming is a kind of attack that is difficult to defend. When the jammer transmits the jamming signal, the nodes covered by the jamming signal will suffer for their transmission. The interactions between the jammer and the source nodes are complex. Thus this article models the interactions between the jammer and the source node as an evolutionary game-theoretical model.The game model of jamming is formulated, and then the evolutionary analysis of the game model is done. The game model is solved using replicator dynamics, and also the solution is verified by conducting various simulations. It can also be verified by using a stability diagram of replicator dynamics. The equilibrium points are analyzed to check whether that is an evolutionarily stable strategy. Extensive numerical simulation experiments have been carried out to analyze the evolution of strategies for source nodes and jammer nodes.

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

Cited by 59 publications

References 49 publications

An intrusion detection mechanism for IPv6-based wireless sensor networks

An intrusion detection mechanism for IPv6-based wireless sensor networks

Optimal cloud assistance policy of end-edge-cloud ecosystem for mitigating edge distributed denial of service attacks

Modeling and simulation of defense game model for jamming attack in wireless sensor networks using evolutionary game theory

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