Multi-Stage Enhanced Zero Trust Intrusion Detection System for Unknown Attack Detection in Internet of Things and Traditional Networks
Bibliographic record
Abstract
Detecting unknown cyberattacks remains an open research problem and a significant challenge for the research community and the security industry. This article tackles the detection of unknown cybersecurity attacks in the Internet of Things (IoT) and traditional networks by categorizing them into two types: entirely new classes of unknown attacks (type-A) and unknown attacks within already known classes (type-B). To address this, we propose a novel multi-stage, multi-layer zero trust architecture for an intrusion detection system (IDS), uniquely designed to handle these attack types. The architecture employs a hybrid methodology that combines two supervised and one unsupervised learning stages in a funnel-like design, significantly advancing current detection capabilities. A key innovation is the layered filtering mechanism, leveraging type-A and type-B attack concepts to systematically classify traffic as malicious unless proven otherwise. Using four benchmark datasets, the proposed system demonstrates significant improvements in accuracy, recall, and error classification rates for unknown attacks, achieving an average accuracy and recall ranging between 88% and 95%. This work offers a robust, scalable framework for enhancing cybersecurity in diverse network environments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".