Multi-Modal Deep Learning for Effective Malicious Webpage Detection
Bibliographic record
Abstract
The pervasive threat of malicious webpages, which can lead to financial loss, data breaches, and malware infections, underscores the need for effective detection methods. Conventional techniques for detecting malicious web content primarily rely on URL-based features or features extracted from various webpage components, employing a single feature vector input into a machine learning model for classifying webpages as benign or malicious. However, these approaches insufficiently address the complexities inherent in malicious webpages. To overcome this limitation, a novel Multi-Modal Deep Learning method for malicious webpage detection is proposed in this study. Three types of automatically extracted features, specifically those derived from the URL, the JavaScript code, and the webpage text, are leveraged. Each feature type is processed by a distinct deep learning model, facilitating a comprehensive analysis of the webpage. The proposed method demonstrates a high degree of effectiveness, achieving an accuracy rate of 97.90% and a false negative rate of a mere 2%. The results highlight the advantages of utilizing multi-modal features and deep learning techniques for detecting malicious webpages. By considering various aspects of web content, the proposed method offers improved accuracy and a more comprehensive understanding of malicious activities, thereby enhancing web user security and effectively mitigating the risks associated with malicious webpages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".