A Data Mining Framework to Predict Cyber Attack for Cyber Security
Bibliographic record
Abstract
Cyber-attacks are exponentially increasing daily with the advancements of technology. Therefore, the detection and prediction of cyber-attacks are very important for every organization that is dealing with sensitive data for business purposes. In this paper, we present a framework on cyber security using a data mining technique to predict cyber-attacks that can be helpful to take proper interventions to reduce the cyber-attacks. The two main components of the framework are the detection and prediction of cyber-attacks. The framework first extracts the patterns related to cyber-attacks from historical data using a J48 decision tree algorithm and then builds a prediction model to predict the future cyber-attacks. We then apply the framework on publicly available cyber security datasets provided by the Canadian Institute of Cybersecurity. In the datasets, several kinds of cyber-attacks are presented including DDoS, Port Scan, Bot, Brute force, SQL Injection, and Heartbleed. The proposed framework correctly detects the cyber-attacks and provides the patterns related to cyber-attacks. The overall accuracy of the proposed prediction model to detect cyber-attacks is around 99%. The extracted patterns of the prediction model on historical data can be applied to predict any future cyber-attacks. The experimental results of the prediction model indicate the superiority of the model to detect any future cyber-attacks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".