A Detailed Classification of Routing Attacks Against RPL in Internet of Things
Why this work is in the frame
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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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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