Protecting hosts against attacks in IMAGO system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A mobile agent is a piece of software which is able to migrate and execute on a remote host. The host may accept the agents without knowing the result in advance of executing the agents. Malicious agents may launch denial of service (DoS) attacks which may cause resource exhaustion or system deadlock. In fact, poorly written mobile agents or executing some agents in special cases can lead to the same result, so that authorized access of system services or resources does harm to the host as well. Mobile agents are coded in a variety of scripting or interpreted languages, and simply using semantic analysis for the predetection of potential hazards cannot provide a general solution. Discerning effectively, and thereby protecting the hosts against such potential threats, is necessary in mobile agent research. The paper presents a mobile agent system called the IMAGO (intelligent mobile agent gliding online) system and related security architecture. It introduces a way of protecting hosts against such possible threats. The paper demonstrates how the IMAGO system works against several typical threats with an insignificant overhead by the means of several techniques.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it