Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey

Valliammal, N.

doi:10.19101/ijatee.2018.547030

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Naïve bayes is a supervised machine learning based approach that work based on Bayesian theorem and its advantage is of simplicity and easy of computation (Al-Shehariand Alsowail, 2021;Al-Mhiuai et al, 2020). K means clustering works by discovering clusters in the data object with k being number of clusters that have been formed (Valliammal and Shaju, 2018;Gayathri, Sajanhar and Xiang, 2020). When clustering is done, objects in a particular cluster will have similar characteristics.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-based Framework for Detecting Malicious Insider-Inspired Cyberattacks Activities in Organisations

Chengetanai,

Chandigere,

Chengetanai

et al. 2024

iccws

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Abstract— Cyberattacks are happening at an alarming rate both in developed and developing countries. This is due to more users now being connected to the global village (internet). Significant strides have been taken by organisations to protect information technology assets together with data, by doing defense-in-depth, using firewalls and access control approaches collectively. These approaches work well in detecting attacks by outsider cyber-attackers. In recent cyberattacks the perpetrators have been those within the organisation, as they can easily bypass security measures especially those with high privileges and they can go undetected for quite a long time. We propose a deep learning approach termed Automatic_ IDS_ Deep model (framework) that is infused with intrusion detection systems to give timely detection of malicious activities by those within the organisation. Experiments were conducted and averaging of results was done to determine accuracy, recall, and precision of the proposed model. The model (framework) offers better results on its performance in detecting attacks that are perpetrated within the organisation.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-based Framework for Detecting Malicious Insider-Inspired Cyberattacks Activities in Organisations

Chengetanai,

Chandigere,

Chengetanai

et al. 2024

iccws

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Deep learning techniques to detect cybersecurity attacks: a systematic mapping study

Torre,

Mesadieu,

Chennamaneni

2023

Empir Software Eng

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A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems

Sagar,

Vanmathi

2024

SN COMPUT. SCI.

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Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey

Abstract: ©2018 ACCENTSMany researchers have intention on CPSs and focused on the anomaly detection using deep learning algorithms. On the other hand, such designs and algorithms have their own limitations and challenges for detecting and removing several types of cyber-

Cited by 5 publications

References 16 publications

Deep Learning-based Framework for Detecting Malicious Insider-Inspired Cyberattacks Activities in Organisations

Deep Learning-based Framework for Detecting Malicious Insider-Inspired Cyberattacks Activities in Organisations

Deep learning techniques to detect cybersecurity attacks: a systematic mapping study

A Comprehensive Review on Deep Learning Techniques on Cyber Attacks on Cyber Physical Systems

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