A Lightweight Deep Neural Model for SMS Spam Detection
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
The short messaging service (SMS) is one of the most popular and also most affordable telecommunication services. The popularity and affordability of SMS, however, have made it an ideal target for spamming. Spam is a major nuisance to mobile subscribers, but can also lead to security breaches or criminal activities. In this paper, we propose a novel lightweight deep neural model called Lightweight Gated Recurrent Unit (LGRU) for SMS spam detection. In addition, we incorporate enhancing semantics retrieved from external knowledge (WordNet) to augment the understanding of SMS text inputs for better classification. We compare the performance of our proposed model with that of more than 30 SMS spam classifiers that use various conventional machine learning and deep learning techniques. Experimental results show that our model outperforms these existing classifiers in terms of precision, recall and accuracy. In addition, our model requires fewer training parameters and incurs significantly less training time than state-of-the-art deep learning based classifiers.
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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