A CNN-based Hybrid Model and Architecture for Shilling Attack Detection
Bibliographic record
Abstract
Recommendation systems are widely used in various areas to personalize recommendations and suggestions to users. However, they are vulnerable to shilling attacks in which malicious users try to promote their products or diminish their competitors'. Therefore, detecting shilling attacks can significantly improve the quality of recommender systems and user experience. With the increasing complexity of attacks and changes in attackers' behavior, more advanced approaches are required to find the hidden patterns in data. This paper proposes a CNN-based hybrid model and architecture to integrate self-learning and flexible aspects of CNN with other approaches to enhance the prediction results of shilling attacks. Two benchmark datasets are used in the experimental analysis, the Movie-Lens 100K and Netflix. The performance of the proposed hybrid models is compared to that of the traditional deep learning and machine learning detection methods. Experimental results show that the superiority of hybrid models over individual models depends on the sparsity level of data and divergence of results.
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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 | 0.000 | 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 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".