Securing RPL Using Network Coding: The Chained Secure Mode (CSM)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it