DOIONLINE 
DOIONLINE Publish In |
International Journal of Advance Computational Engineering and Networking (IJACEN)-IJACEN |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-5 ( May, 2022 ) | |||||||||
Paper Title |
Network Intrusion Detection using Deep Learning | |||||||||
Author Name |
Alok Yadav, Aditya Agrawal, Ayush Mittal, Abhishek Pratap Singh Sengar, Atul Kumar | |||||||||
Affilition |
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Pages |
6-9 | |||||||||
Abstract |
Abstract - In recent years, the increased usage of wireless networks for the transfer of enormous amounts of data has resulted in a slew of security threats and privacy issues as a result, a number of preventive and defensive measures, such as intrusion detection systems (IDS), have been developed. In order to secure computer and network systems, intrusion detection measures are essential. However, performance remains a serious concern for a number of IDS. Furthermore, the veracity of available data is questionable. When the feature space grows, the techniques for IDS employing Machine Learning (ML) are greatly influenced. In this paper, we are focused to implement an intrusion detection system using deep learning that can immediately detect the attacks such as probe, U2R, R2L, DOS. The intrusion when emerges is identified using deep learning model called multilayer perceptron trained by NSL-KDD and tested based on parameters accuracy, precision, f- measure and recall. Keywords - Deep learning, Intrusion, One Hot Encoder, NSL-KDD. | |||||||||
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