An overview of Intrusion Detection within an Information System: The Improvment by Process Mining
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
Information Systems handle big amount of data within enterprises by offering the possibility to collect, treat, keep and make information avail- able. To realize these tasks, it is important to secure data from intrusions that can affect confidentiality, availability and integrity of information. Un- fortunately, with the time, technologies are more used and various types of attacks act on it to create intrusion or misuses within Information Systems. Research in intrusion detection field is still looking for solutions of such relevant problems. The purpose of this paper is to present an overview of existing intrusion detection techniques compared to a new issue based on process mining used for event logs analysis to detect abnormal events that occurs on the system. events are classified accordingly to security policy etablished with fuzzy logic to build a set of fuzzy rules, for the definition of normal and abnormal events and then reduce the high level of false alerts.
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.
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 | 0.000 | 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