Enterprise Security: A Community of Interest Based Approach.
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
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".