Efficient Intrusion Detection of Imbalenced Network Traffic using Deep Learning

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Efficient Intrusion Detection of Imbalenced Network Traffic using Deep Learning
Author(s):
Nayan kumar V, Punith kumar M, Tejas Kumar K, Vinod Kumar S, Dr. Aruna M.G
Keywords:
RandomForest, Alxenett, Lstm, DSSTE algorithm
Abstract
In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of normal data. At Cyberspace, using a high level of encryption and making it difficult for NIDS to ensure accuracy and timeliness. Despite decades of development, IDSs still face challenges in improving in detection accuracy. Deep Learning is a branch of Machine learning, whose performance is remarkable and as a hotspot in field of research.This paper involves both machine learning and Deep learning for intrusion detection in imbalanced network traffic.this process a DSSTE algorithm to avoid imbalance problems.initially the training set is preprocessed to modify imbalanced data and features are extracted. Then newly obtained training set is fed to a various classification models to evaluate that proposed DSSTE algorithm outperforms other methods.
Article Details
Unique Paper ID: 156173 Publication Volume & Issue: Volume 9, Issue 2 Page(s): 1048 - 1054
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