Social Engineering Village - The Voice Told Me To Do It
Bibliographic record
Abstract
Corporate colors and logos characteristic of a brand are easily and freely accessed on the network. As consumers we have been advised to distrust an email with these identities. Instead, the voice gives us confidence. When we need help, the voice is there. It is the first thing we hear when we call, it tells us how wonderful and beneficial it is to be associated with that brand. A voice that will never harm us, until now. Identity spoofing is one of the most used social engineering formats to initiate major attacks. But what if cyber-criminals could go further? What would happen if someone could not only impersonate, but actually use the identity of an institution to make an attack on a national level? Is it possible to do this with a minimal investment or without capital? The answer is yes. Daniel Isler is Security Consultant, Bachelor in Arts of Representation, Actor and Scenic Communicator and Voice Over Artist. With more than 10 years of experience as an academic in Acting classes at the University of Valparaíso, UNIACC University and Professional Institute Aiep. He also develops projects in the area of visual arts. With those who have participated in contemporary art festivals in Chile, Argentina, Portugal and Spain. Since 2015 he leads the Social Engineering team at Dreamlab Technologies. Certifications / Competencies: • Advanced Practical Social Engineering, Orlando, FL, United States. • Usable Security, University of Maryland, United States. • Improvisation Summer School, Keith Johnstone Workshop Inc. Calgary, Canada. • French for foreign language, Université de Pau et des Pays de L’adour, Pau, France. • Diploma in commercial speech, dubbing and neutral accentuation, Voces de Marca, Caracas, Venezuela. • Diploma in Digital Photography, Arcos Professional Institute. • Diploma in Audiovisual Language, UNIACC University.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.080 | 0.055 |
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".