Machine learning approach for automated defense against network intrusions
- Published
- Accepted
- Subject Areas
- Adaptive and Self-Organizing Systems, Algorithms and Analysis of Algorithms, Artificial Intelligence, Autonomous Systems, Data Mining and Machine Learning
- Keywords
- intrusion detection, KDD, DDOS, network security, supervised learning
- Copyright
- © 2019 Hamdani et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Machine learning approach for automated defense against network intrusions. PeerJ Preprints 7:e27777v1 https://doi.org/10.7287/peerj.preprints.27777v1
Abstract
With the advent of the internet, there is a major concern regarding the growing number of attacks, where the attacker can target any computing or network resource remotely Also, the exponential shift towards the use of smart-end technology devices, results in various security related concerns, which include detection of anomalous data traffic on the internet. Unravelling legitimate traffic from malignant traffic is a complex task itself. Many attacks affect system resources thereby degenerating their computing performance. In this paper we propose a framework of supervised model implemented using machine learning algorithms which can enhance or aid the existing intrusion detection systems, for detection of variety of attacks. Here KDD (knowledge data and discovery) dataset is used as a benchmark. In accordance with detective abilities, we also analyze their performance, accuracy, alerts-logs and compute their overall detection rate.
These machine learning algorithms are validated and tested in terms of accuracy, precision, true-false positives and negatives. Experimental results show that these methods are effective, generating low false positives and can be operative in building a defense line against network intrusions. Further, we compare these algorithms in terms of various functional parameters
Author Comment
This is a submission to PeerJ Computer Science for review.