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Detecting Network Intrusion through a Deep Learning Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 14 |
| Year of Publication: 2018 |
| Authors: Abhilasha Jayaswal, Romit Nahar |
10.5120/ijca2018916270
|
Abhilasha Jayaswal, Romit Nahar . Detecting Network Intrusion through a Deep Learning Approach. International Journal of Computer Applications. 180, 14 ( Jan 2018), 15-19. DOI=10.5120/ijca2018916270
Abstract
Intrusion Detection: collection of techniques that are used to identify attacks on the computers and network infrastructures. Anomaly detection, which is a key element of intrusion detection. In Anomaly Detection, perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on an approach based on deep learning to develop an effective and flexible network intrusion detection system implemented using self-taught learning on NSL-KDD data set.
References
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- Gustavo Nascimento, Miguel Correia, Anomaly-based Intrusion Detection in Software as a Service
- Fiore, Ugo and Palmieri, Francesco and Castiglione, Aniello and De Santis, Alfredo, 2013, “Network Anomaly Detection with the Restricted Boltzmann Machine”
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- M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” in Computational Intelligence for Security and Defense Applications, 2009. CISDA 2009. IEEE Symposium
- KDD Cup 99, “http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.”
- Quamar Niyaz, Weiqing Sun, Ahmad Y Javaid, and Mansoor Alam, A Deep Learning Approach for Network Intrusion Detection System
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