(Global Virus Emergency Response Team)
Bibliographic record
Abstract
Over the last two years, worms have resurfaced as a major headache, especially for the companies that get hit by them. Worms aren’t new; they have been around since almost the dawn of computing. With the likes of Nimda, CodeRed, and last years quietly successful worm, Opaserv the rules have changed and the stakes are now significantly higher than ever before. This paper will use the SMB Lure design as presented by John Morris of Nortel Networks at VB2002 as a staring point and cover how it can be extended to improve its usefulness, not just to corporates but also to researchers in the AV companies, these improvements will include: • Sample Capture, via custom scripts/tools. • Sample Recognition, MD5 hashes and anti-virus tools and storage. • Integration with other technologies, such as IDS, Integrity Checking, anti-virus and custom scripts and other useful tools. • Automation By the time that VB2003 arrives a prototype system, based on the technologies and methodologies mentioned above will have been running for almost a year, so there should be some very interesting statistics as well as lessons learnt along the way to share…… Early statistics and information obtained using a very early version of this system was used in the article entitled “Are You Being [Opa]Serve[d]? ” in the January 2003 edition of Virus Bulletin magazine. This paper was written for, and presented at, the 2003 Virus Bulletin conference at the Royal Oak, Toronto, Canada on September 25th – 26th 2003. I would welcome any constructive feedback on this paper and its content.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.214 | 0.168 |
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".