Analysis of Malicious IoT Node Placement for Blackhole Attack Detection

Abstract

The rapid expansion of the Internet of Things has introduced significant security challenges, particularly blackhole attacks that disrupt network communication by malicious nodes dropping all received packets. This study presents a controlled case-study analysis of blackhole attack impact in an RPL-based IoT environment, focusing on node 5 as an internal malicious forwarder in IoT networks employing the Routing Protocol for LowPower and Lossy Networks. The evaluation includes reference networks without attackers and networks with node 5 configured as a blackhole attacker. Simulation results using Contiki OS and Cooja simulator demonstrate that node 5 as a blackhole attacker reduces Packet Delivery Ratio to 92.1% with significant increases in power consumption reaching 1.38 mW average. Hop count variations show complete node disconnections compared to reference scenario. These findings provide crucial insights for network designers implementing security mechanisms in resource-constrained IoT environments. Future directions include the application of machine learning and deep learning techniques for enhanced detection capabilities [1, 5].