Abstract
Heterogeneous network is one of the challenges that must be overcome in Internet of Thing Intrusion Detection System (IoT IDS). The difficulty of the IDS significantly is caused by various devices, protocols, and services, that make the network becomes complex and difficult to monitor. Deep learning is one algorithm for classifying data with high accuracy. This research work incorporated Deep Learning into IDS for IoT heterogeneous networks. There are two concerns on IDS with deep learning in heterogeneous IoT networks, i.e.: limited resources and excessive training time. Thus, this paper uses Principle Component Analysis (PCA) as features extraction method to deal with data dimensions so that resource usage and training time will be significantly reduced. The results of the evaluation show that PCA was successful reducing resource usage with less training time of the proposed IDS with deep learning in heterogeneous networks environment. Experiment results show the proposed IDS achieve overall accuracy above 99%.
Highlights
The growth in the number of complex and diverse traffic as well as spreading of device distribution makes Internet of Things (IoT) security even more complex and challenging
This research aims to propose Principle Component Analysis (PCA)-based feature extraction method for IoT-Intrusion Detection System (IDS) in heterogeneous network the proposed method is combined with a deep learning technique to improve the performance of -Internet of Thing Intrusion Detection System (IoT IDS) performance in heterogeneous networks
Incorporating deep learning into IDS for IoT heterogeneous network can increase the performance of accuracy detection
Summary
The growth in the number of complex and diverse (heterogeneous) traffic as well as spreading of device distribution makes Internet of Things (IoT) security even more complex and challenging. The attacks detection in an IoT environment is different from detection systems on conventional networks such as resource limitations, low latency, distribution, scalability, and mobility [1]. It is necessary to design an IoT IDS that can more precisely detect attacks on heterogeneous networks. Deep learning (DL) technique is a potential candidate solution as it has features to identify small changes in a complex system. Diro and Chilamkurti [2] state that traditional machine learning cannot detect complex intrusions, due to training process of traditional machine learning fails to identify small changes in attack scenario, because traditional machine learning cannot extract invisible features of a dataset. The success of deep learning technique in identifying small changes of data such as changes on pixels in image recognition shows its reliability
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