Analysis of Autoencoders for Network Intrusion Detection

As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to acc...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-06, Vol.21 (13), p.4294
Main Authors: Song, Youngrok, Hyun, Sangwon, Cheong, Yun-Gyung
Format: Article
Language:English
Subjects:
Algorithms
autoencoders
Cryptography
Datasets
deep-learning models
IDS
Intelligent networks
Intrusion detection systems
Learning
Malware
Methods
ML-NIDS
model design
Neural networks
NIDS
Public Key Infrastructure
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Summary:As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks (i.e., zero-day attacks) and also alleviate the burden of the laborious labeling task. Although the autoencoders are effective in detecting unknown types of attacks, it takes tremendous time and effort to find the optimal model architecture and hyperparameter settings of the autoencoders that result in the best detection performance. This can be an obstacle that hinders practical applications of autoencoder-based NIDS. To address this challenge, we rigorously study autoencoders using the benchmark datasets, NSL-KDD, IoTID20, and N-BaIoT. We evaluate multiple combinations of different model structures and latent sizes, using a simple autoencoder model. The results indicate that the latent size of an autoencoder model can have a significant impact on the IDS performance.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21134294