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A Machine Learning Based Intrusion Detection System for Mobile Internet of Things.

Amar Amouri1, Vishwa T Alaparthy2, Salvatore D Morgera1

  • 1Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA.

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Summary
This summary is machine-generated.

This study introduces a novel intrusion detection system (IDS) for the Internet of Things (IoT). The system effectively identifies malicious activities like blackhole and DDoS attacks, achieving over 98% detection rates in high-power scenarios.

Keywords:
AMoFIoTWSNintrusion detection systemslinear regressionrandom forest

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Area of Science:

  • Network Security
  • Wireless Communication Systems
  • Cybersecurity for IoT

Background:

  • Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) lack infrastructure, posing security challenges.
  • The Internet of Things (IoT) is a superset of MANETs/WSNs, with distributed nature and limited resources amplifying security concerns.
  • Existing intrusion detection systems (IDS) struggle to adapt to the dynamic and resource-constrained environments of modern wireless networks.

Purpose of the Study:

  • To present a robust, cross-layer intrusion detection system (IDS) tailored for the Internet of Things (IoT) environment.
  • To evaluate the performance of the proposed IDS under extreme network conditions, including varying power levels and node velocities.
  • To characterize the detection capabilities of the IDS against specific malicious activities such as blackhole and distributed denial of service (DDoS) attacks.

Main Methods:

  • A two-stage, cross-layer IDS utilizing dedicated sniffers (DSs) to collect correctly classified instances (CCIs).
  • A heuristic approach based on the accumulated measure of fluctuation (AMoF) derived from CCIs.
  • Super nodes (SNs) perform linear regression on CCIs from multiple DSs to classify nodes as benign or malicious.
  • Testing conducted using Random Waypoint (RWP) and Gauss Markov (GM) mobility models.

Main Results:

  • The proposed IDS achieved detection rates exceeding 98% in high power and high node velocity scenarios.
  • Detection rates decreased to approximately 90% in low power and low node velocity scenarios.
  • The system demonstrated effectiveness in differentiating benign from malicious nodes under various network conditions.

Conclusions:

  • The developed IDS is effective in detecting blackhole and DDoS attacks in IoT networks.
  • Network parameters like power level and node velocity significantly influence IDS detection rates.
  • The cross-layer approach and heuristic analysis provide a promising direction for securing resource-constrained wireless networks.