A study on the security policy improvement using the big data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
조직이 보유한 정보보호시스템들은 모든 취약점, 침입, 자료유출 등을 탐지하는 것을 목적으로 하고 있다. 그에 따라, 기업은 조직구성원들의 모든 행동이 어떤 경로에서든지 기록되고 확인할 수 있도록 하는 시스템들을 지속적으로 도입하고 있다. 반면에 이것을 관리하고 이 시스템들에서 생성되는 보안로그들을 분석하는 것은 더욱 어려워지고 있다. 보안시스템들을 관리하고 로그를 분석하는 대부분의 인원은 현업의 정보유통 프로세스와 중요정보의 관리절차에 대해 사용자, 또는 유출자보다 알기 어렵다. 이러한 현실은 내부정보유출의 심각성을 더 키우고 있다고 할 수 있다. 최근 빅데이터에 관한 연구가 활발히 진행 되면서 다양한 분야에서 성공사례들을 발표하고 있다. 본 연구는 빅데이터 처리기술과 활용사례를 보안 분야에 적용하여, 기존에 분석할 수 없었던 대용량 정보를 좀 더 효과적으로 분석가능하도록 할 수 있었던 사례와 효율적으로 보안관리 업무를 개선할 수 있는 방안을 제시하고자 한다. The information protection systems of company are intended to detect all weak points, intrusion, document drain. All actions of people in company are recorded and can check persistently. On the other hand, what analyze security log generated by these systems becomes more difficult. Most staff who manages the security systems, and analyze log is more incomprehensible than a user or a person of drain for an information distribution process of the work-site operations and the management procedure of the critical information. Such a reality say the serious nature of the internal information leakage that can be brought up more. While the research on the big data proceeds actively recently, the successful cases are being announced in the various areas. This research is going to present the improved big data processing technology and case of the security field.
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.
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 | 0.001 | 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 | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it