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Non-ohmic Devices

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In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
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A Time-efficient Multi-Protocol Probe Scheme for Fine-grain IoT Device Identification.

Dan Yu¹, Peiyang Li¹, Yongle Chen¹

¹College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.

Sensors (Basel, Switzerland)

|April 2, 2020

Summary

This summary is machine-generated.

This study introduces a time-efficient method using reinforcement learning for identifying Internet of Things (IoT) device brands and models. The approach significantly reduces probe time while maintaining high identification accuracy for IoT security.

Keywords:

Internet of Things device identification fine-grain identification multi-protocol probe

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Area of Science:

Computer Science
Network Security
Artificial Intelligence

Background:

The proliferation of Internet of Things (IoT) devices presents significant management and security challenges.
Accurate identification of IoT device brand and model is crucial for effective discovery, monitoring, and protection.
Existing methods struggle to balance the overhead of multi-protocol probes with the required identification granularity.

Purpose of the Study:

To develop a time-efficient multi-protocol probe scheme for fine-grained identification of IoT devices.
To optimize the probe sequence for balancing identification accuracy and efficiency.
To address the challenge of identifying diverse brands and models within IoT device types.

Main Methods:

Modeled banner-based device identification as a Markov decision process (MDP).
Utilized reinforcement learning and a value iteration algorithm to generate an optimal multi-protocol probe sequence.
Extracted optimal probe sequence segments based on an identification accuracy gain threshold.

Main Results:

Reduced webcam brand and model identification time by 50.76%.
Achieved identification accuracies of 90.5% for brand and 92.3% for model.
Demonstrated significant improvements in identification efficiency for other IoT devices like routers and printers.

Conclusions:

The proposed time-efficient multi-protocol probe scheme effectively enhances IoT device identification accuracy and efficiency.
Reinforcement learning offers a viable solution for optimizing probe strategies in complex network environments.
This method provides a scalable approach for managing and securing the growing landscape of IoT devices.