Mimicry Attacks Demystified: What Can Attackers Do to Evade Detection?
Bibliographic record
Abstract
Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate an attack that evades detection while achieving the attackerpsilas goals. If such an attack can be found, it implies that the target detector is vulnerable against mimicry attacks. In this work, we emphasize that there are two components of a buffer overflow attack: the preamble and the exploit. Although the attacker can modify the exploit component easily, the attacker may not be able to prevent preamble from generating anomalous behavior since during preamble stage, the attacker does not have full control. Previous work on mimicry attacks considered an attack to completely evade detection, if the exploit raises no alarms. On the other hand, in this work, we investigate the source of anomalies in both the preamble and the exploit components against two anomaly detectors that monitor four vulnerable UNIX applications. Our experiment results show that preamble can be a source of anomalies, particularly if it is lengthy and anomalous.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".