Explainable artificial intelligence for intrusion detection in IoT networks

Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach

Sharma, Bhawana (Manipal University Jaipur)
Sharma, Lokesh (Manipal University Jaipur)
Lal, C. (TU Delft Cyber Security)
Roy, Satyabrata (Manipal University Jaipur)

2024

The Internet of Things (IoT) is currently seeing tremendous growth due to new technologies and big data. Research in the field of IoT security is an emerging topic. IoT networks are becoming more vulnerable to new assaults as a result of the growth in devices and the production of massive data. In order to recognize the attacks, an intrusion detection system is required. In this work, we suggested a Deep Learning (DL) model for intrusion detection to categorize various attacks in the dataset. We used a filter-based approach to pick out the most important aspects and limit the number of features, and we built two different deep-learning models for intrusion detection. For model training and testing, we used two publicly accessible datasets, NSL-KDD and UNSW-NB 15. First, we applied the dataset on the Deep neural network (DNN) model and then the same dataset on Convolution Neural Network (CNN) model. For both datasets, the DL model had a better accuracy rate. Because DL models are opaque and challenging to comprehend, we applied the idea of explainable Artificial Intelligence (AI) to provide a model explanation. To increase confidence in the DNN model, we applied the explainable AI (XAI) Local Interpretable Model-agnostic Explanations (LIME ) method, and for better understanding, we also applied Shapley Additive Explanations (SHAP).

Convolution neural network
Deep neural network
DL
Intrusion detection system
Local interpretable model-agnostic explanations
Shapley additive explanations
XAI

https://doi.org/10.4233/uuid:caa942c6-2bba-4b57-81d3-d29c6dcabf9f

https://doi.org/10.1016/j.eswa.2023.121751

2024-03-25

0957-4174

Expert Systems with Applications, 238

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2024 Bhawana Sharma, Lokesh Sharma, C. Lal, Satyabrata Roy