Multi-objective Markov-enhanced adaptive whale optimization cybersecurity model for binary and multi-class malware cyberthreat classification
Bibliographic record
Abstract
The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger; the real threat lies in the obfuscation of these cyberattacks, as they constantly change their behavior, making detection more difficult. Numerous researchers and developers have devoted considerable attention to this topic; however, the research field has not yet been fully saturated with high-quality studies that address these problems. For this reason, this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization (MOMEAWO) cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach. The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing, detecting, and classifying the behavior of obfuscated malware within their respective families. The proposed model includes three classification types: binary classification and multi-class classification (e.g., four families and 16 malware families). To evaluate the performance of this model, we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis (CIC-MalMem-2022) that contains balanced data. The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".