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The Internet of Things (IoT) is revolutionizing society by connect- ing people and devices seamlessly and providing enhanced user experience and functionalities. However, the unique properties of IoT networks, such as heterogeneity and non-standardized protocol, have created critical security holes and network mismanagement. We propose a new measurement tool for IoT network data to aid in analyzing and classifying such network traffic. We use evidence from both security and machine learning research, which suggests that the complexity of a dataset can be used as a metric to determine the trustworthiness of data. We test the complexity of IoT networks using Intrinsic Dimensionality (ID), a theoretical complexity mea- surement based on the observation that a few variables can often describe high dimensional datasets. We use ID to evaluate four mod- ern IoT network datasets empirically, showing that, for network and device-level data generated using IoT methodologies, the ID of the data fits into a low dimensional representation; this makes such data amenable to the use of machine learning algorithms for anomaly detection.
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Network Traffic Characteristics of IoT Devices in Smart Homes
Understanding network traffic characteristics of IoT devices plays a critical role in improving both the performance and security of IoT devices, including IoT device identification, classification, and anomaly detection. Although a number of existing research efforts have developed machine-learning based algorithms to help address the challenges in improving the security of IoT devices, none of them have provided detailed studies on the network traffic characteristics of IoT devices. In this paper we collect and analyze the network traffic generated in a typical smart homes environment consisting of a set of common IoT (and non-IoT) devices. We analyze the network traffic characteristics of IoT devices from three complementary aspects: remote network servers and port numbers that IoT devices connect to, flow-level traffic characteristics such as flow duration, and packet-level traffic characteristics such as packet inter-arrival time. Our study provides critical insights into the operational and behavioral characteristics of IoT devices, which can help develop more effective security and performance algorithms for IoT devices.
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- Award ID(s):
- 1662487
- PAR ID:
- 10312067
- Date Published:
- Journal Name:
- 2021 International Conference on Computer Communications and Networks (ICCCN)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCCN52240.2021.9522168
