Securing RPL Using Network Coding: The Chained Secure Mode (CSM)
Bibliographic record
Abstract
Considered the preferred routing protocol for many Internet of Things (IoT) networks, the routing protocol for low-power and lossy networks (RPL) incorporates three security modes to protect the integrity and confidentiality of the routing process: 1) the unsecured mode (UM); 2) preinstalled secure mode (PSM); and 3) the authenticated secure mode (ASM). Both PSM and ASM were originally designed to protect against external routing attacks, in addition to some replay attacks (through an optional replay protection mechanism). However, recent research showed that RPL, even when it operates in PSM, is still vulnerable to many routing attacks, both internal and external. In this article, a novel secure mode for RPL, the chained secure mode (CSM), is proposed using the concept of intraflow network coding (NC). The CSM is designed to enhance RPL’s resiliency and mitigation capability against replay attacks. In addition, CSM allows the integration with external security measures such as intrusion detection systems (IDSs). An evaluation of the proposed CSM, from a security and performance point of view, was conducted and compared against RPL in UM and PSM (with and without the optional replay protection) under several routing attacks: the neighbor attack (NA), wormhole (WH), and CloneID attack (CA), using average packet delivery rate (PDR), end-to-end (E2E) latency, and power consumption as metrics. It showed that CSM has better performance and more enhanced security than both the UM and PSM with the replay protection while mitigating both the NA and WH attacks and significantly reducing the effect of the CA in the investigated scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".