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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