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A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks.
- Source :
-
Information (2078-2489) . Mar2024, Vol. 15 Issue 3, p164. 26p. - Publication Year :
- 2024
-
Abstract
- Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine learning practices. In order to evaluate the pros and cons of the machine-learning-based approach, we replicated a research study that uses a deep neural network model for intrusion detection. The results of our replicated research study expose several systematic problems with the used datasets and evaluation methods. In our experiments, a signature-based intrusion detection system with a minimal setup was able to outperform the tested model even under small traffic changes. Testing the replicated neural network on a new dataset recorded in the same environment with the same attacks using the same tools showed that the accuracy of the neural network dropped to 54%. Furthermore, the often-claimed advantage of being able to detect zero-day attacks could not be seen in our experiments. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*INTERNET of things
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 20782489
- Volume :
- 15
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Information (2078-2489)
- Publication Type :
- Academic Journal
- Accession number :
- 176334037
- Full Text :
- https://doi.org/10.3390/info15030164