Enhancing Security in LLNs Using a Hybrid Trust-Based Intrusion Detection System for RPL
Bibliographic record
Abstract
An extensive worldwide network known as the Internet of Things (IoT) links different electronic devices and facilitates easy communication and group work. This interdependency is especially apparent in Low Power and Lossy Networks (LLNs), where resource-constrained devices adhere to specified protocols for effective communication. Such systems frequently use Routing Protocol for LLNs (RPL). Nevertheless, due to its basic simplicity, there are numerous ways to exploit it, thereby compromising network security. It is also difficult to carry out complex computational operations on LLNs due to their resource constraints. A highly developed system called the Trust-Based Intrusion Detection System for RPL (TIDSRPL) is presented in this research study. Complex trust computations are offloaded to the root node by TIDSRPL, which assesses node trust based on network behavior. Reduce the possibility of resource depletion with this strategic transfer that preserves energy, storage, and computational resources at the node level. Comparative analysis with the default RPL Objective Function (OF), MRHOF-RPL, demonstrates TIDSRPL’s superior efficacy in detecting and isolating malicious nodes engaged in Sinkhole, Selective forwarding, and Sybil attacks. Notably, TIDSRPL exhibits a 20-35% reduction in average packet loss ratio and attains 33-45% greater energy efficiency compared to MRHOF-RPL, reinforcing its robustness in securing LLN operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".