Securing Mobile-Agent Systems through Collaboration
Bibliographic record
Abstract
Mobile agents are self-contained programs that migrate among computing devices to achieve tasks on behalf of users. Autonomous and mobile agents make it easier to develop complex distributed systems. Many applications can benefit greatly from employing mobile agents, especially e-commerce. For instance, mobile agents can travel from one e-shop to another, collecting offers based on customers’ preferences. Mobile agents have been used to develop systems for telecommunication networks, monitoring, information retrieval, and parallel computing. Characteristics of mobile agents, however, introduce new security issues which require carefully designed solutions. On the one hand, malicious agents may violate privacy, attack integrity, and monopolize hosts’ resources. On the other hand, malicious hosts may manipulate agents’ memory, return wrong results from system calls, and deny access to necessary resources. This has motivated research focused on devising techniques to address the security of mobile-agent systems. This chapter surveys the techniques securing mobile-agent systems. The survey categorizes the techniques based on the degree of collaboration used to achieve security. This categorization resembles the difference between this chapter and other surveys in the literature where categorization is on the basis of entities/ parts protected and underlying methodologies used for protection. This survey shows the importance of collaboration in enhancing security and discusses its implications and challenges.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".