A Revised Attack Taxonomy for a New Generation of Smart Attacks
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
The last years have seen an unprecedented amount of attacks. Intrusions on IT-Systems are rising constantly - both from a quantitative as well as a qualitative point of view. Well-known examples like the hack of the Sony Playstation Network or the compromise of RSA are just some samples of high-quality attack vectors. Since these Smart Attacks are specifically designed to permeate state of the art technologies, current systems like Intrusion Detection Systems (IDSs) are failing to guarantee an adequate protection. In order to improve the protection, a comprehensive analysis of Smart Attacks needs to be performed to provide a basis against emerging threats.Following these ideas and inspired by the original definition of the term Advanced Persistent Threat (APT) given by U.S. Department of Defense, this publication starts with defining the terms, primarily the group of Smart Attacks. Thereafter, individual facets of Smart Attacks are presented in more detail, before recent examples are illustrated and classified using these dimensions. Next to this, current taxonomies are presented including their individual shortcomings. Our revised taxonomy is introduced, specifically addressing the latest generation of Smart Attacks. The different classes of our taxonomy are discussed, showing how to address the specifics of sophisticated, modern attacks. Finally, some ideas of addressing Smart Attacks are presented.
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 | 0.007 |
| 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 it