Towards Incorporating Discrete-Event Systems in Secure Software Development
Bibliographic record
Abstract
When designers and developers create software they often overlook issues related to security. Ideally, protection of the program from illegal usage would be considered at each stage of this program's life cycle. The proposition put forward here is to augment intrusion detection systems (IDSs) and employ them as a tool to support secure software development. Many state-based intrusion detection methods share structural and behavioural similarities with the set of processes known as discrete-event systems (DESs). A common structure for modelling DESs is the deterministic finite-state automaton. There exist several compatible anomaly detection techniques which construct finite- state machine models of normal behaviour through the decomposition of associated data (e.g., system calls, HTTP requests) into sequences of events. This paper proposes the application of decentralized DES theory to formally analyze and enhance these approaches to anomaly detection with misuse prevention. Models of misuse attacks are generated in the same manner as the legal usage representation, then augmented and integrated into the program model to prevent the execution of malicious sequences. The technique described herein simultaneously uses anomaly and misuse approaches to prevent and disable attacks before their completion.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".