Paper Details

The escalated growth of the Internet of Things(IoT) has raised the need to detect traffic data in real-timecoming from IoT devices and develop autonomous threat analysis technologies. To cope with constantly changing andevolving new security threat, we need to develop advanced detection techniques that provide both real-time cycleanalysis and high-level detection performance. Signature-based detection, a commonly used technique to detectthreats, is typically good at detecting known threats, but can’t do much to spot the latest changing and evolvingsecurity threats. As a way to overcome this limitation, some have suggested semi-supervised learning solutions thatuse machine learning algorithms to learn traffic from IoT devices only with normal data and then determine whetherthere are anomaly compared to normal traffic. But the methods are not optimized for detecting IoT traffic anomaly,and are not very useful. Due to different traffic patterns from each household, the traffic anomaly detection system forthe IoT devices needs to have a household-specific and, furthermore, a device-specific detection model. Conventionalmethods require detection systems that minimize the potential challenges because users have to checks anomalyscores from each household and device and set up the threshold setting that determines outliers. Furthermore,existing methods fail to take into account the performance of the routers that will execute the detection model.Generally, the size and complexity of the model correlate positively with detection performance, but the larger andmore complex the becomes, the more load on the performance-constrained router device can occur. Accordingly, weneed to come up with a methodology that can minimize the load on the router devices while maintaining greatdetection performance and efficiently handle fast traffic. In this paper, we extract network traffic informationcommunicated by IoT devices from the router to which the devices are connected, and then create vectors based onstatistics that can represent unique patterns of network traffic of each device. Subsequently, we propose a method todetect traffic anomaly of each device and identify abnormal network traffic without user intervention.

Sungtaek OH, Woong GO, “A Study on the Deep Learning-based Intrusion Detection Modle for the IoT Network”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 8, Issue 11, pp. 31-38, November 2021.