Internet of things (IoT) has opened new horizons in connecting all sorts of devices to the internet. However, continuous demand for connectivity increases the cybersecurity risks, rendering IoT devices more prone to cyberattacks. At the same time, rapid advances in Deep Learning (DL)-based algorithms provide state-of-the-art results in many classification tasks, including classification of network traffic or system logs. That said, deep learning algorithms are considered computationally expensive as they require substantial processing and storage capacity. Sadly, IoT devices have limited resources, making renowned DL models hard to implement in this environment. In this paper we present a Residual Neural Network inspired DL-based Intrusion Detection System (IDS) that incorporates weight pruning to make the model more compact in size and resource consumption. Additionally, the proposed system leverages feature selection algorithms to reduce the feature-space size. The model was trained on the NSL-KDD dataset benchmark. Experimental results show that the proposed system is effective, being able to classify network traffic with an F1 score of up to 98.9% before the pruning and an F1 score of up to 97.5% after pruning 90% of network weights.
Incident Detection with Pruned Residual Multilayer Perceptron Networks
Mohamad Soubra,Marek-Kisiel Dorohinicki,Marcin Kurdziel,M. Zachara
Published 2023 in Conference on Computer Science and Information Systems
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- Publication year
2023
- Venue
Conference on Computer Science and Information Systems
- Publication date
2023-09-17
- Fields of study
Computer Science, Engineering
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