Attack Classification of Imbalanced Intrusion Data for IoT network Using Ensemble Learning-based Deep Neural Network

With the increase in popularity of Internet of Things (IoT) and rise in interconnected devices, the need to foster effective security mechanism to handle vulnerabilities and risks in IoT networks has become evident. Security mechanisms such as Intrusion Detection System (IDS) are designed and deploy...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 10; no. 13; p. 1
Main Authors: Thakkar, Ankit, Lohiya, Ritika
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Bagging
Class Imbalance
Class Weights
Classification
Classification algorithms
Cybersecurity
Datasets
Deep learning
Deep Neural Network
Ensemble learning
Internet of Things
Intrusion detection
Intrusion Detection System
Intrusion detection systems
Machine learning
Neural networks
Rank tests
Training
ISSN:2327-4662, 2327-4662
Online Access:Get full text
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Summary:With the increase in popularity of Internet of Things (IoT) and rise in interconnected devices, the need to foster effective security mechanism to handle vulnerabilities and risks in IoT networks has become evident. Security mechanisms such as Intrusion Detection System (IDS) are designed and deployed in IoT network environment to ensure security and prevent unauthorized access to system and resources. Moreover, there have been efforts to design IDS using various Deep Learning (DL) techniques, as these techniques possess intriguing characteristic of representing data with high abstraction. However, intrusion detection datasets used in literature possess imbalance class distribution, which is one of the challenging issue in developing coherent and potent intrusion detection and classification system. In this paper, we aim to address class imbalance problem using ensemble learning approach, namely, Bagging classifier, that uses Deep Neural Network (DNN) as base estimator. Here, in the proposed approach, the training process of DNN is influenced by including class weights that advocates to create balanced training subsets for DNN. The desirability and merit of the proposed approach can be considered as two-fold as it aims to achieve generalization along with addressing the class imbalance problem in intrusion detection datasets. The performance of the proposed approach is evaluated using four intrusion detection datasets, namely, NSL-KDD, UNSW_NB-15, CIC-IDS-2017, and BoT-IoT. Result analysis of the proposed approach is illustrated using various evaluation metrics, namely, accuracy, precision, recall, f-score, and False Positive Rate (FPR). Moreover, results of the proposed approach are also statistically tested using Wilcoxon signed-rank test.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3244810