A security framework for agent‐based systems
Bibliographic record
Abstract
Purpose – This paper aims to address some security issues in open systems such as service-oriented applications and grid computing. It proposes a security framework for these systems taking a trust viewpoint. The objective is to equip the entities in these systems with mechanisms allowing them to decide about trusting or not each other before starting transactions. Design/methodology/approach – In this paper, the entities of open systems (web services, virtual organizations, etc.) are designed as software autonomous agents equipped with advanced communication and reasoning capabilities. Agents interact with one another by communicating using public dialogue game-based protocols and strategies on how to use these protocols. These strategies are private to individual agents, and are defined in terms of dialogue games with conditions. Agents use their reasoning capabilities to evaluate these conditions and deploy their strategies. Agents compute the trust they have in other agents, represented as a subjective quantitative value, using direct and indirect interaction histories with these other agents and the notion of social networks. Findings – The paper finds that trust is subject to many parameters such as the number of interactions between agents, the size of the social network, and the timely relevance of information. Combining these parameters provides a comprehensive trust model. The proposed framework is proved to be computationally efficient and simulations show that it can be used to detect malicious entities. Originality/value – The paper proposes different protocols and strategies for trust computation and different parameters to consider when computing this trust. It proposes an efficient algorithm for this computation and a prototype simulating it.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".