Machine Learning vs Deep Learning for Anomaly Detection and Categorization in Multi-cloud Environments
Bibliographic record
Abstract
Detecting intrusions is a critical issue in cyberse-curity. One way to overcome this issue is to build efficient and robust Network Intrusion Detection Systems (NIDS) using existing Machine Learning (ML) algorithms. Such an approach has been proposed in the literature and has been shown to perform well. However, a comparative analysis of the performance of ML and Deep Learning (DL) based NIDS for both detection and categorization of intrusions is still needed. This paper investigates the performance of ML and DL models for both intrusion detection and categorization. We use the publicly available Canadian Institute of Cybersecurity Intrusion Detection System 2017 (CICIDS-2017) dataset to train and test ML and DL models. We apply three traditional ML models, namely, Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), and three DL models − 1-D Convolutional Neural Network (ConvlD), Recurrent Neural Network (RNN), and a two-staged model that combines an unsupervised Dense Autoencoders (DAE) for pre-training and an Artificial Neural Network (ANN) for classification. Our results demonstrate that RF is the best performing ML model with a detection accuracy of 99.5% and DAE-ANN is the best performing DL model with a detection accuracy of 98.7%. We also show the advantages of using a stepwise multi-classification over a classical single-stage multi-classification. Finally, we observe that RF outperforms DAE-ANN in categorization with detection rates of 91.35 % and 84.66 %, respectively.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".