Bibliographic record
Abstract
In recent years, malicious software (malware) has become one of the most insidious threats in computer security. However, this is arguably not the result of increased sophistication in malware design or attack strategies, but rather of the increased presence of computers and computer networks within every aspect of society. In this paper, we address and defend the commonly shared point of view that the worst is very much yet to come. We introduce an aim-oriented performance theory for malware and malware attacks, within which we identify some of the performance criteria for measuring their "goodness" with respect to some of the typical objectives for which they are currently used. We also use the OODA loop model, a well-known paradigm of command and control borrowed from military doctrine, as a tool for organising and reasoning about the behavioural characteristics of malware and orchestrated attacks using it. We then identify and discuss particular areas of malware design and deployment strategy in which very little development has been seen in the past, and that are likely sources of increased future malware threats. Finally, we discuss how standard optimisation techniques could be applied to malware design, in order to allow even moderately equipped malicious attackers to quickly converge towards optimal malware attack strategies and tools fine-tuned for the current Internet
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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".