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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".