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

  • 0Department Computer Science, College of Computer Science and Information Systems, Najran University, 61441, Najran, Saudi Arabia.
Scientific Reports +

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August 5, 2024

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August 5, 2024

View abstract on PubMed

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.

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.

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