Implementation and Comparison of various ML and Deep Learning Techniques for Network Intrusion Detection System
Shreevatsa T P
, Radhika K R
Machine Learning Algorithms, Deep Learning, Intrusion Detection System
With the advent of internet to almost every walk of contemporary life, the need for internet security is ever increasing. The threat to the system can be a Denial-of-Service attack or a Worm attack or a Fuzzers attack and causing the integrity of the system to collapse or to compromise. Thus, to monitor the system network, Intrusion Detection System are extensively used in the cybersecurity domain. In order to detect attacks and subsequently thwart such future attacks, the project uses Machine Learning and Deep Learning algorithms to find and detect such attacks. The project is built on UNSW-NB15 (modern substitute to the well-known KDD99 Dataset) to analyze and classify normal and abnormal packets. The paper also sheds some light on few Machine Learning and Deep Learning Algorithms and makes a comparative study on them.
"Implementation and Comparison of various ML and Deep Learning Techniques for Network Intrusion Detection System", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 9, page no.833 - 838, September-2022, Available :https://ijsdr.org/papers/IJSDR2209133.pdf
Volume 7
Issue 9,
September-2022
Pages : 833 - 838
Paper Reg. ID: IJSDR_201529
Published Paper Id: IJSDR2209133
Downloads: 000347184
Research Area: Information Technology
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
