Integrate long short-term memory networks and support vector machine for Wi-Fi indoor intrusion detection
Bibliographic record
Abstract
Wi-Fi sensing based indoor intrusion detection system is a system that can detect mobile entities without attaching any device to them. To address the potential effects caused by the complex amplitude and phase variations of current detection methods, this paper proposes a new method for indoor intrusion detection (LSID: Long Short-Term Memory and Support Vector Machine Intrusion Detection) that fuses long short-term memory networks and support vector machines.The LSID method adopts a new eigenvalue modeling approach, which utilizes a long short-term memory network that can learn the temporal features and can capture the long-term dependencies of temporal signals, the difference between the true value of the channel state information and the predicted value of the long short-term memory neural network is used as the eigenvalue, which can more accurately capture the intruder's influence on the signal state information. The detection method is validated in the school laboratory environment after many experiments, and the final detection accuracy reaches 99.21%. Through the comparison of multiple groups of experiments, the results show that the LSID method has the effectiveness and feasibility, and the accuracy is significantly improved compared with other intrusion detection methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".