Manipulation through Online Sexual Behavior: Exemplifying the Importance of Human Factor in Intelligence and Counterintelligence in the Big Data Era
Bibliographic record
Abstract
As we spend more and more time online, Internet-based virtual spaces are becoming a central component of our daily life and activities. This shift of human activities from offline to online spaces has major impacts for national security. Consequently, cyberspace became a new field of operation for intelligence and counter-intelligence services worldwide. While massive efforts are made to further strategies based on surveys and analyses of large datasets, cybersecurity protocols can be impacted tremendously by individual behaviors. This is particularly the case of online sexual behavior, which can be easily manipulated by malevolent agents. This paper will describe some of the general characteristics of sexual cyberbehaviors. We will then identify some of the main threats related to sexual cyberbehavior (specifically risks of blackmailing, risks associated with the use of online dating sites, and risks associated with the consumption of online pornography), as well as the main targets in terms of population from an intelligence/counter-intelligence perspective. Finally, we will propose some possible counter-measures, that could be implemented to reduce the security risks related to online sexual behavior.
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 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 | 0.002 | 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 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".