Unsupervised ML Based Detection of Malicious Web Sessions with Automated Feature Selection: Design and Real-World Validation
Bibliographic record
Abstract
As Web bot technologies continue to evolve, the task of separating human Web sessions from those generated by malicious bots becomes increasingly more challenging. To date, many research studies have proposed the use of advanced ML-based methods as automated means of differentiating between Web bot and genuine human sessions. Unfortunately, most of these studies overlook the importance of adequate feature selection during the dataset preprocessing stage. Namely, instead of making the process of feature selection automated and optimized to each particular dataset, these studies generally resort to the use of the same fixed set of hand-picked Web-session attributes. It is well known, however, that suboptimal approach to feature selection is likely to result in suboptimal performance of the respective ML algorithm and, consequently, of the entire system. The main contributions of our work are as follows: First, we propose the use of Gradient Boosting Technique to automatically identify the most significant Web-session features (out of an extensive list of 119 possible attributes) for any given dataset. Second, we integrate this automated features selection technique into a system for Web-session classification based on the unsupervised Self-Organizing Map (SOM) algorithm. Third, we validate the performance of the integrated system on a recent real-world dataset which has been collected during a confirmed large-scale attack on our home institution. The obtained experimental results not only verify that our proposed system is highly effective in identifying malicious Web-sessions, but they also help us better understand the nature and scale of the conducted attack itself.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".