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2019 | OriginalPaper | Chapter

Long Short-Term Memory-Based Recurrent Neural Network Approach for Intrusion Detection

Authors : Nishanth Rajkumar, Austen D’Souza, Sagaya Alex, G. Jaspher W. Kathrine

Published in: Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Publisher: Springer International Publishing

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Abstract

Intrusion detection is very essential in the field of information security. The cornerstone of an Intrusion Detection System (IDS) is to accurately identify different attacks in a network. In this paper, a deep learning system to detect intrusions is proposed. The existing recurrent neural network (RNN-IDS) based IDS is expanded to include Long Short term memory (LSTM) and the results are compared. The binary classification performance of the RNN-IDS is tested with various learning rates and using different number of hidden nodes. The results show that by integrating LSTM with RNN-IDS, the accuracy of intrusion prediction has improved against the benchmark dataset.

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previous chapter Scaled Conjugate Gradient Algorithm and SVM for Iris Liveness Detection
next chapter Video Frame Interpolation Using Deep Convolutional Neural Network
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Metadata
Title
Long Short-Term Memory-Based Recurrent Neural Network Approach for Intrusion Detection
Authors
Nishanth Rajkumar
Austen D’Souza
Sagaya Alex
G. Jaspher W. Kathrine
Copyright Year
2019
Book
Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

Print ISBN: 978-3-030-00664-8

Electronic ISBN: 978-3-030-00665-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-00665-5_81