A Revised Attack Taxonomy for a New Generation of Smart Attacks
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.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".