AI-Driven Cybersecurity: Enhancing Threat Detection and Mitigation with Deep Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it