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

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.