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Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT.

Hani Alshahrani^1,2, Mohd Anjum³, Sana Shahab⁴

¹Department Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.

Scientific Reports

|August 5, 2024

Summary

This summary is machine-generated.

This study introduces the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) to efficiently detect intruders in Internet of Things (IoT) networks. The novel method significantly reduces computation time for accurate intrusion detection.

Keywords:

Adversary classification Deep recurrent learning Internet of Things Intrusion detection system Service failure

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Area of Science:

Cybersecurity
Network Security
Internet of Things (IoT)

Background:

Unauthorized access to Internet of Things (IoT) networks poses significant security risks.
Existing intrusion detection systems often suffer from high computation times, hindering accurate and timely intruder identification.
Intermediary access during IoT data transmission introduces vulnerabilities.

Purpose of the Study:

To develop an efficient intrusion detection method for IoT networks.
To address the challenge of high computation time in existing systems.
To improve the accuracy and speed of identifying unauthorized access in IoT environments.

Main Methods:

Implementation of the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM).
Utilizing concealed service sessions to detect anonymous interrupts.
Training the system with parameters including origin, session access demands, and user legitimacy.
Employing a deep recurrent learning approach for data processing to identify service failures and breaches.

Main Results:

The IAU-S-DM method demonstrated a service failure rate of 10.65%.
Achieved a detection precision of 14.63% and a detection time improvement of 15.54%.
The classification ratio reached 20.51%, indicating effective intruder activity recognition.

Conclusions:

The IAU-S-DM method offers a computationally efficient solution for detecting intrusions in IoT networks.
Deep recurrent learning enhances the identification of service failures and breaches, improving detection rates.
The method effectively utilizes the TON-IoT dataset for identifying intruder activities and validating system consistency.