A fine-tuning of decision tree classifier for ransomware detection based on memory data
Bibliographic record
Abstract
Ransomware has evolved into a pervasive and extremely disruptive cybersecurity threat, causing substantial operational and financial damage to individuals and businesses. This article explores the critical domain of Ransomware detection and employs Machine Learning (ML) classifiers, particularly Decision Tree (DT), for Ransomware detection. The article also delves into the usefulness of DT in identifying Ransomware attacks, leveraging the innate ability of DT to recognize complex patterns within datasets. Instead of merely introducing DT as a detection method, we adopt a comprehensive approach, emphasizing the importance of fine-tuning DT hyperparameters. The optimization of these parameters is essential for maximizing the DT capability to identify Ransomware threats accurately. The obfuscated-MalMem2022 dataset, which is well-known for its extensive and challenging Ransomware-related data, was utilized to evaluate the effectiveness of DT in detecting Ransomware. The implementation uses the versatile Python programming language, renowned for its efficiency and adaptability in data analysis and ML tasks. Notably, the DT classifier consistently outperforms other classifiers in Ransomware detection, including K-Nearest Neighbors, Gradient Boosting Tree, Naive Bayes, and Linear Support Vector Classifier. For instance, the DT demonstrated exceptional effectiveness in distinguishing between Ransomware and benign data, as evidenced by its remarkable accuracy of 99.97%.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".