Zigbee-Based Intrusion Detection System for Wormhole Attack in Internet of Things
Bibliographic record
Abstract
Internet of Things (IoT) network security is impacted significantly by routing errors present in IoT network.In IoT, routing errors caused by wormhole attacks affect badly on network performance.Out of several attacks, the wormhole attack is one of the most uncompromising attacks in IoT network.The wormhole attack can be launched using any protocol and also against the encrypted traffic hence it is very challenging to address it.In addition to altering routing algorithms by introducing incorrect routes, the wormhole attack also attacks location-dependent protocols, making routing algorithms useless.This paper presents the development of an Intrusion Detection System (IDS) for detecting and removing wormhole attack.Temperature sensors, Zigbee communication module, and Arduino modules are used in the implementation of the IDS for the detection of wormhole attacks with hardware.In order to detect attacks, a sudden increase in transmitted packets and changes in routing tables are taken into account.In the proposed system, the Received Signal Strength Indicator (RSSI) values of transmitted packets are used to detect the attack and attacker nodes.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".