We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

The Internet of Things (IoT) is expected to count for a large part of the Internet traffic and its impact on the network is still widely unknown. It is therefore essential to study the IoT Traffic in order to characterize its properties and evaluate its performances. In this paper, we propose a novel IoT traffic generator called IoTTGen. We model the IoT traffic and we generate synthetic traffic for smart home and bio-medical IoT environments. We also extracted anomalous IoT traffic from a real dataset and study the IoT traffic properties by computing the entropy value of traffic parameters. Our generator succeeds in capturing the characteristics of the IoT traffic, which can be visually observed on Behavior Shape graphs. Our generator can also serve to describe the main IoT traffic properties and also to detect IoT traffic anomalies.

In this paper, we propose a novel approach to evaluate risks for Internet of Things (IoT). As the impact of the IoT traffic on the Internet is still largely unknown, we characterize IoT traffic in smart home environment and then generate synthetic IoT traffic in order to compare with IoT malicious traffic. This work is the first step towards understanding the properties of IoT traffic and its impact on the Internet as well as for detecting anomalies in IoT traffic.

This paper proposes an adaptive peer search method that considers the dynamic users’ mobility in location-based peer-to-peer (P2P) networks. In the previous study, the neighbor peer search is periodically executed, and the search area is constantly fixed as a circle regardless of the peers’ moving speed. Therefore, it is difficult to properly acquire on neighbor peers, especially in the situation when peers are moving at high speed. In this paper, we suggest a method to determine the peer search interval and the search area size adaptively depending on the peers’ locations and moving speed. Simulation results show that the proposed method enables peers to search neighbor peers at appropriate intervals and to obtain nearby information efficiently.