Routing Attacks and Mitigation Methods for RPL-Based Internet of Things
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
The recent bloom of Internet of Things (IoT) and its prevalence in many security-sensitive environments made the security of these networks a crucial requirement. Routing in many of IoT networks has been performed using the routing protocol for low power and lossy networks (RPL), due to its energy-efficient mechanisms, secure modes availability, and its adaptivity to work in various environments; hence, RPL security has been the focus of many researchers. This paper presents a comprehensive study of RPL, its known attacks, and the mitigation methods proposed to counter these attacks. We conducted a detailed review of the RPL standard, including a recently proposed modification. Also, we investigated all recently published attacks on RPL and their mitigation methods through the literature. Based on this investigation, and to the best of our knowledge, we introduced a first-of-its-kind classification scheme for the mitigation methods that is based on the techniques used for the mitigation. Furthermore, we thoroughly discussed RPL-based intrusion detection systems (IDSs) and their classifications, highlighting the most recently proposed IDSs.
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 | 0.008 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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