Malicious and Malfunctioning Node Detection via Observed Physical Layer Data

Abstract

There are many mechanisms that can cause inadequate or unreliable information in sensor networks. A user of the network might be interested in detecting and classifying specific sensors nodes causing these problems. Several network layer based trust methods have been developed in previous research to assess these issues; in contrast this work develops a trust protocol based on observations of physical layer data collected by the sensors. Observations of physical layer data are used for decisions and calculations, and are based on just the measurements collected by the sensors. Although this information is packaged and distributed on the network layer, only the physical measurement is considered. This protocol is used to detect faulty nodes operating in the sensor network. The context of this research is Wireless Network Discovery (WND), which refers to modeling all layers of a non-cooperative wireless network. The focus in particular is the localization of transmitters, and detection of sensors affecting the localization. To accomplish this, a model for faulty sensors and two methods of detection are developed. Detection rates are analyzed with Receiver Operating Characteristic (ROC) curves, and the trade-off of detection versus localization error is discussed. Classification between faulty sensors is also considered to determine appropriate response to potential network attacks.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2011
Accession Number
ADA540191

Entities

People

  • Tyler Hardy

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Angle Of Arrival
  • Anomaly Detection
  • Change Detection
  • Detection
  • Detectors
  • False Alarms
  • Global Positioning Systems
  • Literature Surveys
  • Maximum Likelihood Estimation
  • Mobile Phones
  • Sensor Networks
  • Urban Areas
  • Warning Systems
  • Wireless Networks
  • Wireless Sensor Networks

Readers

  • Cybersecurity.
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML