Targeted threat index: characterizing and quantifying politically-motivated targeted malware
Bibliographic record
Abstract
Targeted attacks on civil society and nongovernmental organizations have gone underreported despite the fact that these organizations have been shown to be frequent targets of these attacks. In this paper, we shed light on targeted malware attacks faced by these organizations by studying malicious e-mails received by 10 civil society organizations (the majority of which are from groups related to China and Tibet issues) over a period of 4 years. Our study highlights important properties of malware threats faced by these organizations with implications on how these organizations defend themselves and how we quantify these threats. We find that the technical sophistication of malware we observe is fairly low, with more effort placed on socially engineering the e-mail content. Based on this observation, we develop the Targeted Threat Index (TTI), a metric which incorporates both social engineering and technical sophistication when assessing the risk of malware threats. We demonstrate that this metric is more effective than simple technical sophistication for identifying malware threats with the highest potential to successfully compromise victims. We also discuss how education efforts focused on changing user behaviour can help prevent compromise. For two of the three Tibetan groups in our study simple steps such as avoiding the use of email attachments could cut document-based malware threats delivered through e-mail that we observed by up to 95%.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".