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An Enhanced Anomaly Detection in Networked Systems through Deep Learning Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 66 |
| Year of Publication: 2025 |
| Authors: Chaitali V. Chaudhary, S. Vanitha |
10.5120/ijca2025924451
|
Chaitali V. Chaudhary, S. Vanitha . An Enhanced Anomaly Detection in Networked Systems through Deep Learning Model. International Journal of Computer Applications. 186, 66 ( Feb 2025), 1-6. DOI=10.5120/ijca2025924451
Abstract
In the rapidly evolving digital landscape, the proliferation of interconnected devices and networks has introduced unprecedented security challenges. As cyber threats evolve in complexity there is a pressing need for robust intrusion detection systems (IDS) capable of safeguarding against a wide range of attacks. This paper explores the efficacy of utilizing deep learning techniques, specifically a multi-scale convolutional neural network (M-CNN) for detecting network intrusions using the CSE-CIC-IDS2018[9] dataset. The study focuses on meticulous data preprocessing techniques to enhance model performance and presents a streamlined approach for intrusion detection. Through comprehensive experimentation and evaluation, the proposed M-CNN model demonstrates high accuracy, precision, recall, and F1-score for detecting various types of network intrusions comapred to other studies, highlighting its effectiveness in mitigating cyber threats in modern networks.
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