Deep Learning Based Network Intrusion Detection System
Lalitha V G
, Sandeep Varma N
Deep Learning, Intrusion Detection System, LTSM, CNN
Increase in the network intrusion attacks has raised in the recent few years which has increased the focus on confidentiality and protection. As a result of high technology, internet security attacks are getting complicated and the present detection systems aren’t adequate to deal with this problem. Smart and powerful intrusion disclosure system can be implemented to deal with this issue. In this suggested system, the deep structured learning approaches provide various methods and they can detect the intrusions in the network. CNN and LSTM are used to design a smart detection system that is capable enough to detect different network intrusions. Here we apply CNN and LSTM algorithms and train the model using NSL-KDD dataset. We then evaluate its performance of individual models. Our experimental outcomes show that the execution of the LSTM model is beyond that of the CNN model when tested on NSL-KDD dataset.
"Deep Learning Based Network Intrusion Detection System", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 9, page no.467 - 472, September-2022, Available :https://ijsdr.org/papers/IJSDR2209080.pdf
Volume 7
Issue 9,
September-2022
Pages : 467 - 472
Paper Reg. ID: IJSDR_201639
Published Paper Id: IJSDR2209080
Downloads: 000347216
Research Area: Engineering
Country: Bangalore, Karnataka, India
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
