Improving Cybersecurity in Business Settings: The Challenges and Solutions of Machine Learning
Bibliographic record
Abstract
Business-critical cybersecurity faces a new foe in the form of new and advanced threats, such as zero-day exploits and dynamic malware, which have risen in recent years, threatening the very foundations of cybersecurity solutions that rely on signature-based threat detection, which is both reactive and less adaptive. To address these constraints, the paper offers aMachine Learning (ML) powered cybersecurity system that uses both supervised and unsupervised ML to categorize attacks and respond to them in (near) real time. Ittrains the proposed system with big datasets containing abnormalities to detect and classify anomalies using Support Vector Machine (SVM), Random Forest (RF), Isolation Forest (IF), and other techniques. Principal findings include the proposed system's 95% accuracy and 93% precision, as well as a 5% lower false positive rate than older systems. These also demonstrates a higher detection rate for malware (97%) and zero-day exploits (90%). Furthermore, the proposed system achieves faster inference times (5ms) with fewer computational resources, demonstrating its potential for increasing company security and dynamically reacting to present and future cyber threats.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".