Anomaly-Based DNN Model for Intrusion Detection in IoT and Model Explanation

Explainable Artificial Intelligence

Conference paper (2023)

Authors

Bhawana Sharma Manipal University Jaipur
Lokesh Sharma Manipal University Jaipur
C. Lal Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2023 Bhawana Sharma, Lokesh Sharma, C. Lal
DOI: https://doi.org/10.1007/978-981-19-6661-3_28
SVM DT ML KNN LIME DNN Intrusion detection system (IDS) DL
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

IoT has gained immense popularity recently with advancements in technologies and big data. IoT network is dynamically increasing with the addition of devices, and the big data is generated within the network, making the network vulnerable to attacks. Thus, network security is essential, and an intrusion detection system is needed. In this paper, we proposed a deep learning-based model for detecting intrusions or attacks in IoT networks. We constructed a DNN model, applied a filter method for feature reduction, and tuned the model with different parameters. We also compared the performance of DNN with other machine learning techniques in terms of accuracy, and the proposed DNN model with weight decay of 0.0001 and dropout rate of 0.01 achieved an accuracy of 0.993, and the reduced loss on the NSL-KDD dataset having five classes. DL models are a black box and hard to understand, so we explained the model predictions using LIME.