Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks

Fragmentation is a routine part of communication in 6LoWPANbased IoT networks, needed to accommodate small frame sizes on constrained wireless links. But this process comes with an overlooked trade-off: individual fragments are typically stored and processed before their legitimacy is confirmed. For attackers, this offers a low-cost but powerful way to exhaust memory, jam communication, or confuse packet reassembly—all with just a few well-timed transmissions. In this work, we explore a defense strategy that takes a more adaptive, behavior-aware approach to this problem. Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms. The first tracks how each node behaves over time, rewarding consistent and successful interactions while quickly penalizing suspicious or failing patterns. The second checks the integrity of packet fragments using a chained hash, allowing incomplete or manipulated sequences to be caught early, before they can occupy memory or waste processing time. We put this system to the test using a set of targeted attack simulations, including early fragment injection, replayed headers, and flooding with fake data. Across all scenarios, Predictive-CSM preserved network delivery and maintained energy efficiency, even under pressure. Rather than relying on heavyweight cryptography or rigid filters, this approach allows constrained devices to adapt their defenses in real time—based on what they observe, not just what they’re told. In that way, it offers a step forward for securing fragmented communication in real-world IoT systems