Detecting Shilling Attacks Using Hybrid Deep Learning Models
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
Recommendation systems play a significant role in alleviating information overload in the digital world. They provide suggestions to users based on past symmetric activities or behaviors. Being heavily dependent on users’ behavior, they tend to be vulnerable to shilling attacks. Therefore, protecting them from attacks’ effects is highly important. As shilling attacks have features of a large number of ratings and increasing complexity in attack models, deep learning methods become proper alternatives for more accurate attack detections. This paper proposes a hybrid model of two different neural networks, convolutional and recurrent neural networks, to detect shilling attacks efficiently. The proposed deep learning model utilizes the transformed network architecture for undertaking the attributes derived from user-rated profiles. This architecture enables modeling of the temporal and spatial information in the recommendation system’s ratings. The hybrid model overcomes the limitations of the existing shilling attack deep-learning methods to enhance the recommendation systems’ efficiency and robustness. Experimental results show that the hybrid model results in better predictions on the Movie-Lens 100 K and Netflix datasets by accurately detecting most of the obfuscated attacks compared to the state-of-art deep learning algorithms used for investigation.
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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 | 0.001 |
| 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 it