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Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection.

Jorge Granjal1, João M Silva2, Nuno Lourenço3

  • 1Centre for Informatics and Systems, Department of Informatics Engineering, University of Coimbra Polo 2, 3030-290 Coimbra, Portugal. jgranjal@dei.uc.pt.

Sensors (Basel, Switzerland)
|August 1, 2018
PubMed
Summary

This study introduces an Intrusion Detection System (IDS) framework for securing Internet of Things (IoT) devices using CoAP communication. The proposed anomaly-based IDS effectively detects and prevents Denial of Service (DoS) attacks and protocol-specific threats.

Keywords:
6LoWPANCoAPanomaly detectioninternet-integrated sensor networksintrusion detection

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Area of Science:

  • Computer Science
  • Network Security
  • Internet of Things (IoT)

Background:

  • Internet of Things (IoT) applications rely heavily on security for widespread adoption.
  • IoT devices communicating over the internet are vulnerable to various cyber threats and attacks.
  • Existing security measures may not adequately address the unique challenges of IoT communication protocols like CoAP.

Purpose of the Study:

  • To propose and evaluate an Intrusion Detection System (IDS) framework tailored for Internet-integrated CoAP environments.
  • To implement and assess the effectiveness of anomaly-based intrusion detection against Denial of Service (DoS) attacks.
  • To specifically target and mitigate attacks against the 6LoWPAN and CoAP communication protocols.

Main Methods:

  • Development of a novel IDS framework for CoAP communication environments.
  • Implementation of an anomaly-based detection mechanism within the framework.
  • Experimental evaluation of the framework's effectiveness against DoS and protocol-specific attacks.
  • Analysis of detection accuracy, recall, and F-Measure for both multi-class and binary classification problems.

Main Results:

  • The proposed IDS framework demonstrates viability in protecting devices against targeted attacks.
  • Achieved 93% accuracy for the multi-class problem (identifying specific intrusions).
  • For the binary class problem (recognizing compromised devices), achieved 92% accuracy with 98% recall and F-Measure.

Conclusions:

  • The anomaly-based IDS framework offers a promising solution for securing CoAP-based IoT communications.
  • This research presents the first known approach using anomaly detection for application-layer and DoS attacks in 6LoWPAN and CoAP environments.
  • The findings suggest that the proposed method can significantly enhance the security posture of IoT devices.