A Multiclass Approach for Network Intrusion Detection using Convolutional Neural Networks
Shashank Shekhar
, Abhinav Mittra
Network Intrusion Detection System, Machine Learning, Convolutional Neural Networks, UNSW NB-15
The immense popularity of Internet of Things (IoT) and Cloud based applications have resulted in huge volumes of network traffic. Different versions of operating systems, multiple protocols and concurrent users contribute significantly towards the ever increasing computer security threats. Traditional methods involving shallow learning tech- niques like Random Forest, Naive Bayes, etc. have been instrumental in advancing the study of network intrusion detection. However, as and when the network data expands in size and complexity, deep learning algorithms are required to tackle the ongoing network security challenges. Deep learning methods are intrinsically capable of handling enormous data and their performance increases with increasing supply of the same. The proposed work details the configuration of a multi-class classifier using Convolutional Neural Networks. UNSW NB-15, a modern dataset comprising of nine contemporary attack types is used to evaluate the effectiveness of the proposed approach. Results indicate that the proposed approach has exhibited a reasonably valid precision and recall percentage as compared to the preexisting methods.
"A Multiclass Approach for Network Intrusion Detection using Convolutional Neural Networks", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 4, page no.253 - 262, April-2020, Available :https://ijsdr.org/papers/IJSDR2004044.pdf
Volume 5
Issue 4,
April-2020
Pages : 253 - 262
Paper Reg. ID: IJSDR_191637
Published Paper Id: IJSDR2004044
Downloads: 000347206
Research Area: Engineering
Country: Manipal, Udupi, Karnataka, India
DOI: http://doi.one/10.1729/Journal.23883
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
