Cyberattack Detection via Artificial Intelligence in Cloud Computing Environment

N.Suresh

doi:https://doi.org/10.59232/CYS-V1I1P105

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2023 | Article Id: CYS-V1I1P105 DOI: https://doi.org/10.59232/CYS-V1I1P105

Cyberattack Detection via Artificial Intelligence in Cloud Computing Environment

N.Suresh

Received	Revised	Accepted	Published
04 Jun 2023	16 Jun 2023	02 Jul 2023	05 Jul 2023

Citation

N.Suresh. “Cyberattack Detection via Artificial Intelligence in Cloud Computing Environment.” DS Journal of Cyber Security, vol. 1, no. 1, pp. 46-52, 2023.

Abstract

Cyberattack detection is the ability of IT firms to quickly and efficiently identify threats to the network, apps, or other network assets. The first step in creating a successful cyber detection and response strategy is understanding the risks that exist in the online environment. In cyberspace, every method utilised to defend a business's assets, personnel, and operations from online threats is referred to as cyber security. Due to more complex networks and more frequent and sophisticated attacks, organisations need various cyber security solutions to reduce their cyber risk. A deep learning-based cyberattack detection system is proposed in this paper for detecting cyberattacks in cloud environments. Prior to reducing the dimensions, independent component analysis is used to extract the relevant characteristics. As a result, LSTM with multi-head attention is used to categorise different sorts of cyberattacks. The deep learning model's energy usage and detection time will be assessed and contrasted with alternative approaches. We demonstrate through experimental findings that our suggested framework not only distinguishes various cyberattacks but also achieves a high level of detection accuracy (up to 99.1%).

Keywords

Cyber security, Cyberattack, Intrusion detection, Dimension reduction, Multi-Head LSTM, Deep Learning.

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