Volume & Issue no: Volume 9, Issue 5, September - October 2020
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Title: |
Analysis and Classification of Intrusion Detection for Synthetic Neural Networks using Machine Language Strategies |
Author Name: |
Manjunath H , Dr S Saravana kumar |
Abstract: |
Abstract
In the recent yearsdue to the increased throughput and
the multi-uniformity of behaviors, the existing network is
complex. An intrusion detection system is a critical
component for reliable management of information. The
advancement in system hardware field and raise in data
growth related in new fields with respect to the deep
learning technology along with intrusion detection
systems. Learning data presentation studied through deep
learning which a part of Machine learning. To be
successful, network intrusion detection systems, which are
part of the layered protection scheme, must be able to
fulfill certain organizational objectives. This research
paper describes the investigations performed on various
neural network architectures using a variety of intrusion
detection algorithms. New supervised algorithms in
Intrusion Detection System (IDS) have been implemented
that have faster convergence and better performance. The
goal of this research work is to introduce a new balance
of artificial neural networks.
Keywords: -Denial of Service, Artificial neural network,
Malicious, Intrusion detection system. |
Cite this article: |
Manjunath H , Dr S Saravana kumar , "
Analysis and Classification of Intrusion Detection for Synthetic Neural Networks using Machine Language Strategies " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 9, Issue 5, September - October 2020 , pp.
005-009 , ISSN 2278-6856.
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