A Low Power Cyber-Attack Detection and Isolation Mechanism for Wireless Sensor Network
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
Wireless sensor networks (WSNs) are effective tools in many mission-critical applications, such as health care, defence applications, Intelligent Transportation System (ITS), smart grid and industrial condition monitoring. Low power consumption is the main attractive feature of WSNs, hence, protocols and algorithms implemented in WSNs should always maintain low power operation. Cybersecurity of WSNs in mission- critical applications is one of the major design aspects of these networks. However, implementing security mechanisms in WSNs is a challenging task due to the limited computation and power resources of the sensor nodes. Therefore, WSN security mechanisms should not only focus on maintaining high reliability and throughput needed by mission- critical applications, but also should maintain low power operation. In this paper, we develop a low power WSN cybersecurity mechanism suitable for mission-critical applications. Our mechanism can detect and isolate various attacks, such as denial of sleep, forge and replay attacks in an energy efficient way. Simulation results show that our mechanism can outperform existing techniques in terms of power consumption and reliability.
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 | 0.000 | 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 | 0.001 | 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 it