Technological and Human Factors of Malware Attacks
Bibliographic record
Abstract
The success (or failure) of malware attacks depends upon both technological and human factors. The most security-conscious users are susceptible to unknown vulnerabilities, and even the best security mechanisms can be circumvented as a result of user actions. Although there has been significant research on the technical aspects of malware attacks and defence, there has been much less research on how users interact with both malware and current malware defences. This article describes a field study designed to examine the interactions between users, antivirus (AV) software, and malware as they occur on deployed systems. In a fashion similar to medical studies that evaluate the efficacy of a particular treatment, our experiment aimed to assess the performance of AV software and the human risk factors of malware attacks. The 4-month study involved 50 home users who agreed to use laptops that were instrumented to monitor for possible malware attacks and gather data on user behaviour. This study provided some very interesting, non-intuitive insights into the efficacy of AV software and human risk factors. AV performance was found to be lower under real-life conditions compared to tests conducted in controlled conditions. Moreover, computer expertise, volume of network usage, and peer-to-peer activity were found to be significant correlates of malware attacks. We assert that this work shows the viability and the merits of evaluating security products, techniques, and strategies to protect systems through long-term field studies with greater ecological validity than can be achieved through other means.
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.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".