Convolutional Neural Networks with LSTM for Intrusion Detection

11 pages•Published: March 9, 2020

Abstract

A variety of attacks are regularly attempted at network infrastructure. With the increasing development of artificial intelligence algorithms, it has become effective to prevent network intrusion for more than two decades. Deep learning methods can achieve high accuracy with a low false alarm rate to detect network intrusions. A novel approach using a hybrid algorithm of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) is introduced in this paper to provide improved intrusion detection. This bidirectional algorithm showed the highest known accuracy of 99.70% on a standard dataset known as NSL KDD. The performance of this algorithm is measured using precision, false positive, F1 score, and recall which found promising for deployment on live network infrastructure.

Keyphrases: convolutional neural networks, intrusion detection, networks, security

In: Gordon Lee and Ying Jin (editors). Proceedings of 35th International Conference on Computers and Their Applications, vol 69, pages 69-79.

Links:	https://easychair.org/publications/paper/cXbs
	https://doi.org/10.29007/j35r

BibTeX entry

@inproceedings{CATA2020:Convolutional_Neural_Networks_with,
  author    = {Mostofa Ahsan and Kendall Nygard},
  title     = {Convolutional Neural Networks with LSTM for Intrusion Detection},
  booktitle = {Proceedings of 35th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {69},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/cXbs},
  doi       = {10.29007/j35r},
  pages     = {69-79},
  year      = {2020}}

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