An Intrusion Detection System based on Deep Belief Networks

Othmane Belarbi, Aftab Khan, Pietro Carnelli, Theodoros Spyridopoulos
7/5/2022
cs.CRcs.LG

No official code found. Best implementation: othmbela/dbn-based-nids (93 stars, Apache-2.0, last commit 2022-10-27).

Abstract

The rapid growth of connected devices has led to the proliferation of novel cyber-security threats known as zero-day attacks. Traditional behaviour-based IDS rely on DNN to detect these attacks. The quality of the dataset used to train the DNN plays a critical role in the detection performance, with underrepresented samples causing poor performances. In this paper, we develop and evaluate the performance of DBN on detecting cyber-attacks within a network of connected devices. The CICIDS2017 dataset was used to train and evaluate the performance of our proposed DBN approach. Several class balancing techniques were applied and evaluated. Lastly, we compare our approach against a conventional MLP model and the existing state-of-the-art. Our proposed DBN approach shows competitive and promising results, with significant performance improvement on the detection of attacks underrepresented in the training dataset.

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Code Implementations(1)

othmbela/dbn-based-nidsCommunity100%
Apache-2.0

An Intrusion Detection System based on Deep Belief Networks

9317Python, MakefileMay 13, 20223 years agoApache-2.0
cicids2017deep-belief-networksdeep-learningintrusion-detection-system

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Cite this paper

@article{belarbi2022intrusion,
  title  = {An Intrusion Detection System based on Deep Belief Networks},
  author = {Othmane Belarbi and Aftab Khan and Pietro Carnelli and Theodoros Spyridopoulos},
  year   = {2022},
  eprint = {2207.02117},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.02117v1}
}

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