Predicting and mitigating cyber threats through data mining and machine learning
Bibliographic record
Abstract
With cyber threats evolving alongside technological progress, strengthening network resilience to combat security vulnerabilities is crucial. This research extends cyber-crime analysis with an innovative approach, utilizing data mining and machine learning to not only predict cyber incidents but also reinforce network robustness. We introduce a real-time data collection framework to provide up-to-date cyberattack data, addressing current research limitations. By analyzing collected attack data, we identified temporal correlations in attack volumes across consecutive time frames. Our predictive model, developed using advanced machine learning and deep learning techniques, forecasts the frequency of cyber-attacks within specific time windows, demonstrating over a 15% improvement in accuracy compared to conventional baseline models. The methodologies employed include the use of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) for capturing complex patterns in time series data, and the integration of a sliding window technique to transform raw data into a format suitable for supervised learning. Our experiments evaluated the performance of various models, including ARIMA, Random Forest, Support Vector Regression, and K-Nearest Neighbors Regression, across multiple scenarios. Furthermore, we developed a Power BI platform for visualizing global cyber-attack trends, providing valuable insights for enhancing cybersecurity defences. Our research demonstrates that cyber incidents are not entirely random, and advanced AI tools can significantly enhance cybersecurity defences by analyzing patterns and trends from previous instances. This comprehensive approach not only improves prediction accuracy but also offers a robust framework for reducing the risk and impact of future cyber-crimes through enhanced detection and prediction capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".