A Comparative Study of Machine Learning and Deep Learning in Network Anomaly-Based Intrusion Detection Systems
Date
2020-12
Authors
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
Institute of Electrical and Electronics Engineers Inc.
Series Info
Proceedings of ICCES 2020 - 2020 15th International Conference on Computer Engineering and Systems 15 December 2020, Article number 9334553 15th International Conference on Computer Engineering and Systems, ICCES 2020;Virtual, Cairo; Egypt; 15 December 2020 through 16 December 2020; Category numberCFP2054A-ART; Code 166892;
Scientific Journal Rankings
Abstract
This paper presents a comparative study of Machine learning and Deep learning models used in anomaly-based network intrusion detection systems. The paper has presented an overview of the previous work done in the field of ML and DL IDS, then an overview of the used datasets in reviewed literature was presented. Moreover, ML and DL models were tested on the KDD-99 dataset, and performance results were presented, compared, and discussed. Finally, areas of future research of critical importance are proposed by the authors. © 2020 IEEE.
Description
Scopus
Keywords
university, comparative, deep learning, intrusion detection, machine learning