Gradient boosting algorithm for early detection of unknown internet of things devices

الملخص EN

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 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% .