LISF: A Security Framework for Internet of Things (IoT) Integrated Distributed Applications

Abstract

Distributed applications where Internet of Things (IoT) technology integrated are vulnerable to different kinds of attacks. Machine learning algorithms are widely used to detect intrusions in such applications. However, there is need for an effective unsupervised learning approach which can detect known and also unknown attacks. Towards this end, in this paper, we proposed a framework to protect security of IoT integrated architectures that are distributed in nature. Our framework is named Learning based IoT Security Framework (LISF). The framework is designed to have machine learning based security to IoT integrated use cases. Since IoT networks cause network traffic that is to be monitored and protected from external attacks, the proposed system uses deep learning technique for automatic detection of cyber-attacks. Particularly, the system exploits deep autoencoder which comprises of encoder and decoder for automatic detection of different kinds of intrusions. It is based on unsupervised learning which is crucial for distributed environments where network flows cannot have sophisticated training samples. We proposed an algorithm named Deep Autoencoder based Cyber Attack Detection (DAE-CAD). Experiments are made using IoT use case dataset known as UNSW-NB15. Our empirical results revealed that DAE-CAD outperforms existing methods with highest accuracy 91.36%.