Recent worms: a survey and trends
Bibliographic record
Abstract
In this paper, we present a broad overview of recent worm activity. Virus information repositories, such as the Network Associates ' Virus Information Library, contain over 4500 different entries (through the first quarter of 2003). While many of these entries are interesting, a great number of them are now simply historical and a large percentage of them are completely derivative in nature. However, these virus information repositories are the best source of material on the breadth of malicious code, including worms. This paper is meant to provide worm researchers with a high-level roadmap to the vast body of virus and worm information. After sifting through hundreds of entries, we present only those that we considered breakthrough or novel, primarily from a technical perspective. As a result, we found ourselves omitting some of the most notorious worms simply because they lacked any original aspects. It is our hope that others in the community who need to get up to speed in the worm literature can benefit from this survey. While this study does not contain any original research, it provides an overview of worms using a truly breadth-first approach, which has been lacking in the existing worm literature. From this raw data, we have also extracted a number of broad quantitative and qualitative trends that we have found to be interesting. We believe that a workshop discussion of these, and other thoughts, will be engaging and informative.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".