On the imbalance of the security problem space and its expected consequences
Bibliographic record
Abstract
Purpose This paper aims to report on the results of an analysis of the computer security problem space, to suggest the areas with highest potential for making progress in the attacker‐defender game, and to propose questions for future research. Design/methodology/approach The decomposition of the attacker‐defender game into technological, human, and social factors enables one to analyze the concentration of public research efforts by defenders. First, representative activities are selected, then each activity is mapped into the technological, human and social (THS) basis. Afterwards, citation databases are used to estimate the relative volume of publications on each selected activity in the science and engineering communities. Finally, drawing on a number of relevant theories in organizational theory, sociology, and political science, avenues for exploring the social dimension by the defenders are discussed. Findings The analysis suggests that over 94 percent of the public research in computer security has been concentrated on technological advances. Yet attackers seem to employ more and more human and social factors in their attacks. The social organization of the attackers allows them to achieve the results not possible otherwise, shifting the balance in their favour. It is suggested that the scope of research should be broadened, to involve organizational behavior and structure as well as social capital aspects that are currently not high on computer security research agenda. Research limitations/implications The queries limit the search to public content written in the English language only. Since the authors are concerned with the relative (rather than absolute) volume of each activity, it is an open question whether this limitation biases the results. Practical implications As the arms race in computer security progresses, social factors may become or already are increasingly important. The side that capitalizes on them sooner may gain the competitive advantage. Originality/value A simple method for gauging the focus of research efforts in the computer security community and for considering computer security problem space through the lens of social sciences is developed.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".