Intelligent Cyber Threat Analytics and Prediction Model for Secure Cloud Communication Systems
Bibliographic record
Abstract
Cloud-Based communication systems face increasing cybersecurity threats due to their dynamic and high-dimensional data environments. Existing detection methods often struggle to achieve both scalability and high accuracy in cyber threat identification while maintaining computational efficiency. To address these challenges, this paper presents an Intelligent Cyber Threat Analytics and Prediction (ICT-AP) model that integrates Random Forest for feature selection, Linear Discriminant Analysis (LDA) for supervised dimensionality reduction, and the Isolation Forest algorithm for efficient cyber threat detection within a three-tier architecture. The ICT-AP model is validated using the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS 2017) dataset. Experimental results show that the model achieves high precision (greater than 0.93), recall (greater than 0.89), and a stable Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of approximately 0.90, even with limited training data. The proposed method outperforms existing approaches while maintaining low computational overhead, making it suitable for real-world virtualized 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".