A BERT Deep Learning Model for Arabic Spam Detection
Bibliographic record
Abstract
Spam messages pose a significant cybersecurity threat, leading to phishing attacks, fraud, and privacy breaches. Traditional spam detection methods, such as rule-based filtering and statistical models, often fail to capture the evolving and complex nature of spam messages. In this paper, we propose an Arabic spam detection model leveraging BERT (Bidirectional Encoder Representations from Transformers), a deep learning-based NLP model. Our approach enhances classification accuracy by utilizing contextual text representations specific to the Arabic language. We preprocess Arabic text using AraBERT tokenization and fine-tune the BERT-based model on a balanced dataset of Arabic spam and ham messages. Experimental results demonstrate that our model achieves high accuracy (98%), outperforming traditional machine learning and deep learning approaches. This research highlights the potential of transformer-based models in Arabic spam filtering, paving the way for more efficient and robust detection systems.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".