A layered security architecture based on cyber kill chain against advanced persistent threats
Bibliographic record
Abstract
Inherently, static traditional defense mechanisms which mostly act successfully in detecting known attacks using techniques such as blacklisting and malware signature detection are insufficient in defending against dynamic and sophisticated advanced persistent threat (APT) cyberattacks. These attacks are usually conducted dynamically in several stages and may use different attack paths simultaneously to accomplish their commission. Cyber kill chain (CKC) framework provides a model for all stages of an intrusion from early reconnaissance to actions on objectives when the attacker's goal is met which could be stealing data, disrupting operations or destroying infrastructure. Achieving the final goal, an adversary must progress all stages successfully. Any disruption at any stage of the attack by the defender would mitigate or cease the intrusion campaign. In this chapter, we align 7D defense model with CKC steps to develop a layered architecture to detected APT actors tactics, techniques and procedures in each step of CKC. This model can be applied by defenders to plan resilient defense and mitigation strategies against prospective APT actors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".