We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks.
- Authors
Schrötter, Max; Niemann, Andreas; Schnor, Bettina
- 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.
- Subjects
ARTIFICIAL neural networks; INTERNET of things; MACHINE learning
- Publication
Information (2078-2489), 2024, Vol 15, Issue 3, p164
- ISSN
2078-2489
- Publication type
Article
- DOI
10.3390/info15030164