Advanced network security with an integrated trust-based intrusion detection system for routing protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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