Research on decision optimization of network security expert system based on multi-source heterogeneous data
Bibliographic record
Abstract
With the prosperous development of the Internet, the open network environment has also become a breeding ground for hackers, especially in the huge network system, the multi-stage, large-scale and coordinated network attacks have brought great trouble to the traditional defense means.This study designs a network security expert system based on multi-source heterogeneous data based on the characteristics of huge volume of multi-source heterogeneous network security data, heterogeneous format, and diverse semantics.The system contains five hierarchical structures: perception layer, event layer, alarm layer, attack context and attack pattern layer, and attack scenario layer.Petri nets are used for network security risk analysis and assessment to overcome the shortcomings of traditional defenses that become difficult to handle after modeling the attack scenarios.Incorporating the D-S evidence theory, the outputs of multiple decision engines are applied to the network security posture assessment to analyze the network condition from a global perspective and further enhance the effect of network attack classification.In the simulation experiments of simulated attacks, the monitoring information of the network security expert system has autocorrelation coefficients within two times standard deviation ( 0.1 ) after the 0th-order differencing, which indicates that the system is able to accurately assess the potential values of network attacks, such as scanning, brute-force cracking, DoS, and Web.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".