Application of Quality Function Deployment to the Management of Information Physical Security
Bibliographic record
Abstract
Information physical security (IPS) refers to the prevention from intended attacks against all material devices and to the protection against deliberate attacks by supporting and managing related data/information. Information in today's world represents an important asset to be protected and for this reason it is necessary to adopt a suitable method for risk and security management. The Quality Function Deployment (QFD) method was originally developed as a tool capable of ensuring a valuable help in the design of products and services, guaranteeing customer satisfaction and value creation. The core of the method is the set of matrices called the ‘House of Quality’ (HoQ), which relates the Customer Requirements (CRs) with Engineering Characteristics (ECs): in other words, the HoQ is a way of translating customer requirements into design parameters. Numerous studies have demonstrated its use in a wide range of sectors. In particular, its application in the security engineering context has been investigated by means of the House of Security (HoS). Its objective is represented by the classification of the components of a security system in response to different scenarios of voluntary attacks. Based on this, the aim of the study consists in extending such an approach to information physical security. More in detail, the purpose of this paper is the development of a systematic model, based on the HoS and applicable to information physical security, that allows the definition and raking of the vital components of an information physical security system (IPSS). In this way, it is possible to perform a proper cost/benefit analysis, considering a general physical layout of a certain organization so that the results can be wide-ranging and applicable in different contexts.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".