Power Allocation Scheme Based on DRL for CF Massive MIMO Network with UAV

Consul, Prakhar; Budhiraja, Ishan; Garg, Deepak; Bindle, Abhay

doi:10.1007/978-981-19-3796-5_4

Cited by 17 publications

(1 citation statement)

References 15 publications

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“…Misleading up-sides were examined, precision and fault rate. This is demonstrated by authors [68] that it is fundamentally further hard to distinguish the seldom-happened assaults than the normal ones determined by the Bayesian regulations [139] [140].…”

Section: F Improvising the Evaluation Of Modelsmentioning

confidence: 99%

A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems

Gaba,

Budhiraja,

Kumar

et al. 2024

IEEE Access

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In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.

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Section: F Improvising the Evaluation Of Modelsmentioning

confidence: 99%