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

• URL: http://arxiv.org/abs/2506.01767v1
• Date: Mon, 02 Jun 2025 15:15:18 GMT
• Title: Predictive-CSM: Lightweight Fragment Security for 6LoWPAN IoT Networks
• Authors: Somayeh Sobati-M,
• Abstract summary: This work explores a defense strategy that takes a more adaptive, behavior-aware approach to this problem.<n>Our system, called Predictive-CSM, introduces a combination of two lightweight mechanisms.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fragmentation is a routine part of communication in 6LoWPAN-based IoT networks, designed to accommodate small frame sizes on constrained wireless links. However, this process introduces a critical vulnerability fragments are typically stored and processed before their legitimacy is confirmed, allowing attackers to exploit this gap with minimal effort. 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 de vices 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

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