International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 180 - Issue 14 |
Published: Jan 2018 |
Authors: Abhilasha Jayaswal, Romit Nahar |
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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
@article{ 10.5120/ijca2018916270, author = { Abhilasha Jayaswal,Romit Nahar }, title = { Detecting Network Intrusion through a Deep Learning Approach }, journal = { International Journal of Computer Applications }, year = { 2018 }, volume = { 180 }, number = { 14 }, pages = { 15-19 }, doi = { 10.5120/ijca2018916270 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2018 %A Abhilasha Jayaswal %A Romit Nahar %T Detecting Network Intrusion through a Deep Learning Approach%T %J International Journal of Computer Applications %V 180 %N 14 %P 15-19 %R 10.5120/ijca2018916270 %I Foundation of Computer Science (FCS), NY, USA
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.