IntruDetector: a software platform for testing network intrusion detection algorithms
Bibliographic record
Abstract
An intrusion detection system (IDS), that monitors passively specific computing resources, and reports anomalous or intrusive activities, is becoming an important component in the security system of information infrastructure. Algorithms for detecting intrusions are under rapid development, but far from being mature. One interesting and difficult issue is how to study and test a new intrusion detection algorithm against a variety of (perhaps simulated) intrusive activities under realistic background traffic. A flexible and general-purpose platform for testing intrusion detection algorithms is clearly desirable. This paper presents such a software platform, called IntruDetector. With this platform, detection algorithms can be tested directly in a real environment with a wide range of intrusive activities. The data of normal system activities are directly collected from the live environment, and are mixed with intrusive activities that are simulated by hybrid simulation. The main properties of this approach are: (1) the background traffic is realistic; (2) it allows flexible simulation of various types of intrusions; and (3) normal system operation will not be disrupted by virtually simulated destructive intrusions during testing.
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 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 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".