A Deep Learning Approach for Intrusion Detection

International Journal of Computer Science and Engineering

© 2023 by SSRG - IJCSE Journal

Volume 10 Issue 11

Year of Publication : 2023

Authors : T. Sai Harshitha, V. Sreenidhi, Sk. Parveen, P Tejaswini, Yerininti Venkata Narayana

10.14445/23488387/IJCSE-V10I11P106

How to Cite?

T. Sai Harshitha, V. Sreenidhi, Sk. Parveen, P Tejaswini, Yerininti Venkata Narayana, "A Deep Learning Approach for Intrusion Detection," SSRG International Journal of Computer Science and Engineering , vol. 10,  no. 11, pp. 43-48, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I11P106

Abstract:

In the ever-evolving field of cybersecurity, where protecting against unauthorized activities and emerging cyber threats is of paramount importance, this study explores the use of Convolutional Neural Networks (CNNs) to enhance intrusion detection capabilities. Traditional methods often struggle to keep pace with the increasing complexity and diversity of cyber threats, leaving organizations susceptible to data breaches, service disruptions, and financial losses. To address these challenges, CNNs, renowned for their feature extraction capabilities in image analysis, were adapted to the domain of network traffic analysis for precise intrusion detection. Also, conducted a comparative analysis, comparing the CNN approach against Autoencoders, a widely-used unsupervised learning technique for anomaly detection and evaluated the performance of CNN and autoencoders using metrics like accuracy, precision, recall, F1-Score, and AUC-ROC. The current study incorporates the Simargyl2022 dataset to enhance the quality of our results and analyses. This evaluation reveals the strengths and weaknesses of each technique, empowering cybersecurity professionals to make informed decisions about their intrusion detection systems, ultimately strengthening defenses against the ever-evolving cyber threat landscape and ensuring a safer digital world.

Keywords:

Cybersecurity, Intrusion Detection System (IDS), Convolutional Neural Networks (CNN), Deep Learning, Malware, Port Scanning, Denial of Service (DoS), Network Security, Anomaly Detection, Cyberattacks.

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