Enterprise Security: A Community of Interest Based Approach.
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
Enterprise networks today carry a range of mission crit-ical communications. A successful worm attack within an enterprise network can be substantially more devastating to most companies than attacks on the larger Internet. In this paper we explore a brownfield approach to hardening an enterprise network against active malware such as worms. The premise of our approach is that if future communica-tion patterns are constrained to historical “normal ” com-munication patterns, then the ability of malware to exploit vulnerabilities in the enterprise can be severely curtailed. We present techniques for automatically deriving individual host profiles that capture historical communication patterns (i.e., community of interest (COI)) of end hosts within an en-terprise network. Using traces from a large enterprise net-work, we investigate how a range of different security poli-cies based on these profiles impact usability (as valid com-munications may get restricted) and security (how well the policies contain malware). Our evaluations indicate that a simple security policy comprised of our Extended COI-based profile and Relaxed Throttling Discipline can effec-tively contain worm behavior within an enterprise without significantly impairing normal network operation. 1
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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