UGC Approved Journal no 63975(19)

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Published in:

Volume 8 Issue 3
April-2021
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2103336


Registration ID:
307295

Page Number

2674-2679

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Title

Network Intrusion Detection using Linear Regression

Abstract

Detecting intrusions can identify unknown attacks in a network and has been one of the successful ways to enhance network security. The current methods for identifying network anomalies are largely based on traditional machine learning models, such as KNN, SVM, etc. While these methods can accomplish excellent functionality, their accuracy is comparatively low and rely heavily on manual identification of network threats. BAT is a network anomaly detection model that has been developed combining Liner Regression, 3 Layer Neural Network and attention mechanism. Attention mechanism helps in filtering the network flow vector containing packet vectors, which can acquire the significant features for classifying the network traffic. We have also adopted multiple convolutional layers to identify the local features of network traffic. As multiple convolutional layers are used to analyze the data samples, the BAT model is referred to as BAT-MC. The softmax classifier is used for classifying the network activity. We assessed the proposed model on a public standard dataset, and the initial findings suggest that our model has better efficiency than other methods.

Key Words

Network Intrusion Detection using Linear Regression

Cite This Article

"Network Intrusion Detection using Linear Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.2674-2679, April-2021, Available :http://www.jetir.org/papers/JETIR2103336.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Network Intrusion Detection using Linear Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp2674-2679, April-2021, Available at : http://www.jetir.org/papers/JETIR2103336.pdf

Publication Details

Published Paper ID: JETIR2103336
Registration ID: 307295
Published In: Volume 8 | Issue 3 | Year April-2021
DOI (Digital Object Identifier):
Page No: 2674-2679
Country: -, -, - .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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