A Detailed Classification of Routing Attacks Against RPL in Internet of Things
Bibliographic record
Abstract
With the advancement in mobile computing and wireless communications, a new paradigm called Internet of Things is generating a lot of research interest and the industrial revolution. The increasing interest for this paradigm has resulted in the large-scale deployment of Low power and Lossy Networks (LLN), such as wireless sensor networks and home automation systems. These networks are typically composed of many embedded devices with limited power, memory, and processing resources interconnected by a variety of links, such as IEEE 802.15.4 or low-power Wi-Fi. These networks have a wide scope of applications such as industrial monitoring, connected home, healthcare, environmental monitoring, urban sensor networks, energy management, and assets tracking etc.RFC 7228. In order to address the specific properties and constraints of these networks, RPL (Routing Protocol for low power Lossy network) has been developed by the IETF working group [ROLL WG]. RPL is a lightweight, rank based routing protocol. However, this routing protocol is exposed to various attacks which can significantly impact the network resources and its performance. This paper presents an elaborate classification of the possible attacks against RPL in IoT network. Further, we have analyzed and compared the severity of these attacks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".