Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis

Securing Internet of Things networks from cyber security attacks is essential for preventing data loss and safeguarding backbone networks. The resource-constrained nature of the sensor nodes used in the IoT makes them vulnerable to various attacks. Hence, it is important to monitor network traffic information to accurately and promptly identify threats. In this paper, using a machine learning-based framework for learning and detecting such attacks in an IoT network from the network data is proposed. Further, a real IoT network consisting of Raspberry Pi sensor nodes and ZigBee communication modules is built for implementing two cyber attacks. The network traffic information for normal and attack scenarios is collected to evaluate the attack detection performance of learning-based models. We performed a comparison analysis with deep learning and traditional machine learning models. Our evaluation reveals that the proposed features and the machine learning framework can detect attacks with high accuracy from the network traffic information. In particular, the triplet network-based deep learning framework showed promising results in efficiently detecting the attacks from the traffic information with merely a small set of training samples.