Network Intrusion Detection with CNNs: A Comparative Study of Deep Learning and Machine Learning Models
Bibliographic record
Abstract
The exponential increase in internet usage has led to a surge in cyber threats and network attacks, making them critical challenges in today’s digital landscape. Leveraging machine learning techniques has proven effective in detecting and addressing network intrusions. This study focuses on the application of deep learning, particularly convolutional neural networks (CNNs), for improving the detection and classification of intrusions within the NSL-KDD and UNSW-NB15 datasets. The performance of CNN models is compared against several widely-used machine learning algorithms, including Gaussian Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Support Vector Machines (SVM), AdaBoost, XGBoost, CatBoost, and LightGBM. The experimental analysis reveals that CNN-based approaches consistently surpass traditional classifiers, offering enhanced accuracy and robustness in identifying cyber threats.
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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 | 0.000 | 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 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".