Predicting Cyberattack Duration in Next Generation Networks: A Novel Transformer-Based Approach
Bibliographic record
Abstract
In the face of increasingly complex cyberattacks, particularly within 5G networks, accurately predicting the duration of an ongoing attack has become essential for effective attack mitigation. In this work, we tackle this issue by exploring the use of deep learning models to forecast cyber attack duration, thus enabling improved resource allocation and mitigation strategies. Using the diverse UNSW-NB15 dataset, our approach proposes data transformation and the creation of new key features, modeling the problem as a time-series forecasting one to predict the remaining attack time of ongoing attacks. Subsequently, several deep-learning models, suited for time-series data, have been designed. Based on automated hyperparameter tuning and feature engineering, we further refine the developed models. Through extensive simulations, we evaluate the performance of developed approaches in terms of mean absolute error (MAE). Obtained results indicate that the Transformer-based model outperforms other methods by achieving the lowest validation MAE with nearly 60 % reduction compared to the well-known Long Short-Term Memory (LSTM) model, thus showcasing its robustness in accurately predicting the duration of any type of attacks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".