Mobile fog computing security: A user-oriented smart attack defense strategy based on DQL

Tu, Shanshan; Waqas, Muhammad; Meng, Yuan; Rehman, Sadaqat Ur; Ahmad, Iftekhar; Koubâa, Anis; Halim, Zahid; Hanif, Muhammad Imran; Chang, Chin‐Chen; Shi, Chengjie

doi:10.1016/j.comcom.2020.06.019

Cited by 20 publications

(9 citation statements)

References 22 publications

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“…In specific practice, it is necessary to choose the corresponding security strategy according to the constraints of the given defense conditions. For this work, one can refer to the relevant literature 48,49 …”

Section: Discussionmentioning

confidence: 99%

Architecture‐oriented security strategy determination for cyber‐physical systems

Zhao

et al. 2021

Concurrency and Computation

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Cyber‐physical systems (CPSs) usually have a huge number of nodes, and it is very expensive to deploy security defenses at all nodes. Therefore, selecting the most appropriate nodes for protection to ensure system security is an essential issue in CPS. However, how to achieve this in the system design phase remains to be an open challenge. In this paper, we propose a security strategy determination method for CPS design models. In particular, we utilize SysML to model a CPS. To choose the best nodes for security protection, we realize an automatic construction of an attack‐defense tree (ADTree) model from the SysML model by considering the known attacks and defenses. Further, we transform the ADTree model into an atom attack‐defense tree (A2DTree) model and infer the optimal security strategy under a given cost constraint. We implement our method as an open‐source tool and test it with a pump station attack. The experimental results show that the proposed method is reasonable and feasible and can provide theoretical guidance for the formulation of CPS security policies.

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Section: Discussionmentioning

confidence: 99%

Architecture‐oriented security strategy determination for cyber‐physical systems

Zhao

et al. 2021

Concurrency and Computation

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“…Here, e is the edge server. If q e i of the MEC is very long, it means that the MEC is overloaded, and the task will be offloaded to the cloud server for processing [33,34].…”

Section: Characteristics Of Edge Networkmentioning

confidence: 99%

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Xing

Ullah

et al. 2023

PLoS ONE

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Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms.

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“…Methods based on artificial intelligence have been proposed to overcome the limitations of nature-inspired optimization algorithms. The deep Q-learning approach utilizes neural networks like deep learning to help agents to choose the next action [ 23 , 24 ]. As the name suggests, it uses the Q-value (expected reward) to make the decision.…”

Section: Related Workmentioning

confidence: 99%

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

Lim

2022

Sensors

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The Internet of Things applications have become popular because of their lightweight nature and usefulness, which require low latency and response time. Hence, Internet of Things applications are deployed with the fog management layer (software) in closely located edge servers (hardware) as per the requirements. Due to their lightweight properties, Internet of Things applications do not consume many computing resources. Therefore, it is common that a small-scale data center can accommodate thousands of Internet of Things applications. However, in small-scale fog computing environments, task scheduling of applications is limited to offering low latency and response times. In this paper, we propose a latency-aware task scheduling method for Internet of Things applications based on artificial intelligence in small-scale fog computing environments. The core concept of the proposed task scheduling is to use artificial neural networks with partitioning capabilities. With the partitioning technique for artificial neural networks, multiple edge servers are able to learn and calculate hyperparameters in parallel, which reduces scheduling times and service level objectives. Performance evaluation with state-of-the-art studies shows the effectiveness and efficiency of the proposed task scheduling in small-scale fog computing environments while introducing negligible energy consumption.

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Mobile fog computing security: A user-oriented smart attack defense strategy based on DQL

Cited by 20 publications

References 22 publications

Architecture‐oriented security strategy determination for cyber‐physical systems

Architecture‐oriented security strategy determination for cyber‐physical systems

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments

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