Handling User-Oriented Cyber-Attacks: STRIM, a User-Based Security Training Model
Bibliographic record
Abstract
Privacy has become an increasingly rare commodity these days as personal information can never again be private once it enters a social network. That latter became an incubator environment and a carrier for cyber-attacks, either by providing the necessary information about victims or facilitating the task of reaching them. Social media create relationships and trust between individuals, without any authority checking and validating their identity. This paper analyses the different attack vectors and techniques used against end-users to target their organizations. It shows how the available disclosed information can be transformed into a useful image about the organization and the role of the victim inside it. These leaks not only expose users to the risk of cyberattacks, but they also give attackers the opportunity to create personalized cyber-attacks that are difficult to avoid. This paper highlights these user-oriented attacks. It first demonstrates the impact of the disclosed information on the process of the attack formulation in addition to group influence on an individual's vulnerability. Next, the various psychological manipulation factors and cognitive bias behind the user's failure to detecting these attacks demonstrated. This research introduces a theoretical user-based security training model called STRIM, which addresses the above security concerns. It aims to educate and train users to detect, avoid, and report cyberattacks in which they are the primary target. The proposed model is a solution to help organizations establish security-conscious behaviors among their employees.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".