Machine learning algorithms for intruder signal detection and device localization in wireless radio frequency systems
Bibliographic record
Abstract
Radio frequency (RF) wireless systems generate large amounts of data every day on the signal content and the received signal strength (RSS) information. However, limited efforts have been put into analyzing the RF data for security applications, such as the detection of unauthorized transmissions from an intruder. To bridge this gap, the Ph.D. thesis presents machine learning algorithms which detect and localize the intruder devices from their RF transmissions. First, we study the problem of detecting the intruder RF signals in wideband RF traces. An example setup is considered, with the intruder transmitting Wi-Fi signals and there exists interference from Bluetooth and microwave oven signals. We show that the conventional energy-thresholding algorithms are sensitive to noise variations and they require handcrafted parameters for each RF trace. To address this concern, we develop a deep learning solution, which employs convolutional neural networks to perform the signal de- tection. Experiments on both synthetic and real RF traces confirm the superior performance of the proposed solution in terms of the achieved mean average precision. Second, we study the problem of locating the intruder device from the RSS measured passively in the system. We work with the difference of RSS (DRSS), calculated with respect to a reference sensor, in order to handle the unknown transmission power and device heterogeneity of the intruder. The localization problem is formulated as a Gaussian process regression (GP) task to obtain the location estimates and the associated confidence intervals in closed-form. We propose two GP methods which take the stochastic nature of the test DRSS into account and provide more accurate confidence intervals on the test locations than the conventional GP method. Third, to improve the localization accuracy of the proposed GP methods, we present data reconstruction techniques which exploit the low-dimensionality exhibited by the DRSS vectors. Fourth, we study intruder localization for the case when the rate of signal strength decay with distance, also called the path loss exponent, is Gaussian distributed in the area. We propose two low-cost linear least squares estimators for the device location, which employ multilateration on the maximum-likelihood estimates of the distances to the sensors.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".