AI-Driven Cybersecurity: Enhancing Threat Detection and Mitigation with Deep Learning
Bibliographic record
Abstract
AI-driven cybersecurity has emerged as a transformative solution for combating increasingly sophisticated cyber threats. This research proposes an advanced deep learning-based cybersecurity framework aimed at enhancing threat detection and mitigation performance. Leveraging Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) architectures, the proposed model effectively identifies anomalies and classifies potential threats with high accuracy and minimal false positives. The framework was rigorously evaluated using real-time network traffic datasets, demonstrating a notable increase in detection accuracy by 18.5%, achieving a detection accuracy of 97.4%, compared to traditional machine learning methods (78.6%). Additionally, the response time to threats was significantly reduced by 25%, while computational overhead decreased by 30%, enhancing overall system responsiveness. Experimental results further show a 40% reduction in network downtime incidents due to faster identification and proactive mitigation of threats. The proposed AI-driven approach thus provides substantial improvements in security performance metrics, underscoring its potential for robust cybersecurity in dynamic and increasingly sophisticated threat landscapes
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".