NDLI: Enhanced Network Intrusion Detection System

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Enhanced Network Intrusion Detection System

Content Provider	MDPI
Author	Kotecha, Ketan Verma, Raghav Rao, Prahalad V. Prasad, Priyanshu Mishra, Vipul Kumar Badal, Tapas Jain, Divyansh Garg, Deepak Sharma, Shakti
Copyright Year	2021
Description	A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well.
Starting Page	7835
e-ISSN	14248220
DOI	10.3390/s21237835
Journal	Sensors
Issue Number	23
Volume Number	21
Language	English
Publisher	MDPI
Publisher Date	2021-11-25
Access Restriction	Open
Subject Keyword	Sensors Information and Library Science Anomaly Detection Deep Learning Intrusion Detection System Network Security Unsw-nb15
Content Type	Text
Resource Type	Article

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