Zigbee-Based Intrusion Detection System for Wormhole Attack in Internet of Things
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
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it