PERFORMANCE EVALUATION OF ROUTING TABLE OVERLOAD ATTACK IN RPL-BASED IOT NETWORK

ABSTRACT

The IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), which is widely used in IoT networks, is vulnerable to Routing Table Overload Attacks (RTOA) in storage mode. These attacks exploit memory constraints by inserting bogus DAO messages, resulting in routing table saturation and performance deterioration. This study runs extensive simulations with a Python-based SimPy framework spanning three network sizes (50, 100, and 150 nodes) with varied malicious node percentages (10%, 20%, and 30%). The key performance indicators assessed are Packet Delivery Ratio (PDR), Average End-to-End Delay (AE2ED), control packet overhead, energy consumption, and throughput. Simulations show that smaller networks (50 nodes) maintain moderate resilience (PDR = 51%) even during attacks. However, performance suffers greatly in bigger networks. For example, at 150 nodes with 20% malicious nodes, PDR decreased to 0% and throughput to 0 bits/s, indicating full routing failure. Overhead and energy usage also differed between situations, with bigger networks being increasingly vulnerable to assault. These findings underline the vital importance of effective mitigation techniques in RPL storage mode. RTOA's significant impact on large-scale networks indicates that enhanced memory handling, adaptive control messaging, and safe routing techniques are important for future IoT deployments.

Keywords: rpl, internet of things, routing table overload attack, storing mode, performance evaluation, simulation