RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach
Why this work is in the frame
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Bibliographic record
Abstract
In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i.d., as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner.
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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 | 0.000 | 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