Abstract
The pervasive availability of the Internet of Things (IoT) markets lures targets for cyber-attacks since most manufactured IoT devices are usually resource-constrained devices. The first powerful line of IoT network protection from these vulnerabilities is detecting IoT devices especially the unauthorized ones by utilizing machine learning (ML) algorithms. Actually, it is so difficult or even impossible to find individual unknown IoT devices during the setup phase but, knowing their manufacturers is a matter to be deliberate. In this paper, a new method based fingerprints generation is introduced to detect the connected devices in the setup phase. Fingerprints for 21 different IoT devices are generated using devices’ network traffic. The whole produced fingerprints of devices are divided into four groups according to their manufacturers or fingerprints similarity proportion. Gradient Boosting Algorithm is applied to achieve the identified purposes. The proposed method is considered as a preparatory study for early detection of unauthorized. The performance evaluation for the proposed method was calculated based on two metrics: Identification accuracy and F1-score. The average identification accuracy rate was around 98.65%, while the average F1-score was about 99%.
Highlights
Internet of Things (IoT) is defined as a distributed and interconnected network of embedded systems which are communicated through either wired or wireless communication network technologies
Once the IoT vulnerabilities are exploited by attackers, it will give them the ability to control the device, privacy leakage of users, and posing other security concerns like IoT Mirai botnet and launch some types of attacks on IoT network infrastructure which lead to network congestion [4], [5]
The classification of IoT network traffic is a significant aspect of administration systems and current network management since it can be used to retrofit the IoT network with devices that can offer substantial and smart functionalities
Summary
Internet of Things (IoT) is defined as a distributed and interconnected network of embedded systems which are communicated through either wired or wireless communication network technologies. Deep learning was applied using traffic payload data to identify nine IoT devices in [13]. Deep learning was applied to classify fifteen devices into four types and achieves 74.8% as an average accuracy. In [17], a localitysensitive for IoT fingerprints was presented to detect devices when they joined to IoT network. It achieved 90% as an average recall and 93% as an average precision. According to the former studies, a few researchers are concentrated on detect unknown devices especially in the startup phase which regards the best time to detect such devices to make an early decision about their traffic.
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