Extraction and mapping of multidimensional data from computer terminals based on transfer probability matrix Markov chain attack graphs
Bibliographic record
Abstract
The existence of a large number of multi-source heterogeneous hosts and application service types in various zones of the power monitoring system leads to difficulties in extracting comprehensive host attack trace data and the problem of fine-grained deep threat detection.This study combines network attack traces extracted from multi-source logs and stores them in attack trace styles.An attack event description model based on key attributes and behavior sequences is constructed.Based on the vulnerability scoring system, an algorithm is designed to map a general attack graph into an absorbing Markov chain attack graph, which provides a computational basis for the analysis of network attacks by calculating the state transfer probability matrix of the attack graph.Finally, the performance of this paper's method for multi-dimensional data feature extraction is explored in a python experimental simulation environment.The simulation results show that the average mapping time of LSTM model for 7 vulnerabilities is 117ms, while the average mapping time of this paper's algorithm is improved by 37ms compared to the LSTM model.Meanwhile, the accuracy, stability, average false detection rate and positive and negative recall rate also achieve good results, which verifies the validity of this method in the practice of power monitoring system management.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".