Title: Machine learning-based cyber attack recognition model

Authors: Leo John Baptist; Janani Selvam; Divya Midhun Chakkaravarthy

Addresses: Lincoln University College, Wisma Lincoln, No. 12-18, Jalan SS 6/12, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia ' Faculty of Engineering, Lincoln University College, Malaysia ' Faculty of Engineering, Lincoln University College, Malaysia

Abstract: The internet plays an essential role in the daily lives of individuals living in the contemporary world. Because of the volume of users, our private information runs the risk of being disclosed inadvertently somewhere else on the internet. The study of cyber security encompasses a wide range of topics, the most basic of which are the abuse of data and risks to internet security. The proposed system performs an analysis of the dataset and determines if the data in question is typical or out of the ordinary. Following the completion of the dataset analysis, the system makes an effort to recognise and forecast a cyber attack. The ensemble classification approach is used to determine the attack wise detection accuracy found by CADM. The categorisation of network traffic data has been done with the help of the gradient boosting and random forest algorithms. We achieved an accuracy level of 97.4%.

Keywords: cyber attack detection; deep machine learning; DML; smart power grid; data processing.

DOI: 10.1504/IJESDF.2026.150185

International Journal of Electronic Security and Digital Forensics, 2026 Vol.18 No.1, pp.56 - 68

Received: 31 Aug 2023
Accepted: 28 Sep 2023
Published online: 03 Dec 2025 *

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