Security Pattern Detection Through Diagonally Distributed Matrix Matching
Bibliographic record
Abstract
Security requirements should be realized in the design phase of a secure software system. Security patterns are artifacts used to implement security requirements as to security controls and features. The strength in the security of software systems is directly proportional to the number of security patterns used. We can use the number of existing security patterns to measure the security strength of software systems. Therefore, early detection of the absence of security patterns or non-standard security features will tremendously reduce development and maintenance costs. We first convert the security patterns and the software system model into graphs and store them as matrices in the security pattern detection process. Then, we explore and detect security patterns inside the software system using a matching technique. Finally, we remove false positives with the help of a semantic analysis technique. This paper proposes a diagonally distributed matrix matching (DDMM) technique for detecting security patterns. The detection technique uses a standard security pattern matrix (SPM). It selects the main diagonal of the SPM. Then compares it for matching with the diagonals of the target system matrix (TSM) using all possible combinations of diagonal elements. A security pattern detection tool is implemented based on the proposed DDMM technique. The experimental results show sufficient detection accuracy and reasonable time consumption for five java-based software projects with zero false positives.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".