Advanced network security with an integrated trust-based intrusion detection system for routing protocol
Bibliographic record
Abstract
The global network called the Internet of Things (IoT) facilitates communication and teamwork by connecting different electronic devices. This combination is especially seen in low-power and non-local networks (LLNs), where equipment is limited to comply with specified standards for connectivity. These systems often use the LLN routing protocol (RPL). However, due to its simplicity, there are many ways to compromise network security. It is also difficult to perform complex operations in the LLN computation due to limited usage. This work presents an advanced design called a Trust-Based RPL Intrusion Detection System (TIDSRPL). TIDSRPL transfers the complex trust to the root node, and TIDSRPL evaluates the node trust based on the network behavior. Depotentialize resources through this strategic shift that preserves energy, storage, and compute resources at the node level. A comparison with the pre-tuned RPL objective function of minimum rank with hysteresis objective function routing protocol low power and non-local (MRHOF-RPL) network shows that TIDSRPL has the best performance in detecting and classifying malware contained in Sinkhole, choosing to submit, and Sybil objecting. More importantly, TIDSRPL achieves a 20%–35% reduction in average packet loss and a 33%–45% improvement in energy efficiency compared to MRHOF-RPL, improving its stability in LLN protection block efficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".