ISSN :XXXX-XXXX
Edosa Osa, Augustus E. Ibhaze, Erumena C. Ekoko and Patience E. Orukpe
Adv. Artif. Intell. Mach. Learn., 1 (1):38-48
Edosa Osa : University of Benin
Augustus E. Ibhaze : University of Lagos
Erumena C. Ekoko : University of Benin
Patience E. Orukpe : University of Benin
DOI: https://dx.doi.org/10.54364/cybersecurityjournal.2024.1102
Article History: Received on: 19-Feb-24, Accepted on: 20-May-24, Published on: 03-Jun-24
Corresponding Author: Edosa Osa
Email: edosa.osa@uniben.edu
Citation: Edosa Osa, Ibhaze Augustus E., Ekoko Erumena C, Orukpe Patience E. (2024). Development of an Intrusion Detection System Leveraging Deep Learning Model Classification. Adv. Artif. Intell. Mach. Learn., 1 (1 ):38-48
The implementation of Deep
Learning in development of models to act as interventions for addressing the
continuously evolving spate of cybersecurity issues has become a noteworthy
paradigm. This occurs since cyber attacks could be modeled or represented in
terms of data records which can serve as bases for developing intrusion
detection systems. This paper proposes an intrusion detection system that
leverages deep learning techniques for attack classification. The NSL-KDD
benchmark dataset was imported and preprocessed for model development.
Evaluation of the model yielded suitable results in terms of Accuracy,
Precision, Recall and F1-Score.