An SCL-Based Constraint Representation Language for Intrusion Detection
Bibliographic record
Abstract
In this thesis, we have extended the SCL (Structured and Context Language) network protocol description language to describe the complex constraints for the network engineer. Previous SCL developed with the focus of penetration testing and not sufficient for constraint scenarios. The constraint scenarios include multiple-packet with order and environmental information. To address the current limitation of the SCL, we have proposed syntaxes which are declarative in nature. We have studied three different styles of syntaxes to handle constraint scenarios of an IDS (Intrusion detection system). The three syntaxes are based on Java expressions, QUEL and Prolog. We have represented three constraints for command and control systems such as ATC (Air Traffic Control) network using our syntaxes. The same constraints have been previously used by a constraint engine to demonstrate the capability of the IDS. We evaluate each of the syntax based on the four design guidelines for the domain specific language (DSL). The Java-based syntax shows better capability to represent constraints based on four DSL design guidelines. Finally, we show the mapping of the constraints represented in our syntaxes with the low-level DSL (Domain Specific Language) of the constraint engine. The mapping shows our syntaxes has all relevant information to translate into the low-level DSL.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".