Comprehensive Review of Intrusion Detection Techniques: ML and DL in Different Networks
Bibliographic record
Abstract
With the increasing number of new attacks, virtualized and distributed networks require greater attention and investment in cybersecurity. Organizations must rely on effective Intrusion Detection Systems (IDS) to detect both known and novel attacks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques have been widely used for intrusion detection. Several studies have reviewed ML and DL-based detection models, but they often overlook the specific networks targeted by these models. It is crucial to understand not only which methods are effective but also the contexts in which they are effective. This study aims to fill this gap by reviewing and classifying recent contributions based on their target networks. It focuses on three key network types: Cloud Computing (CC), Internet of Things (IoT), and Software-Defined Networks (SDN). Our study emphasizes the importance of thoroughly understanding the strengths and vulnerabilities of a given network, which is an important step towards developing effective ML- and DL-based intrusion detection approaches. We first provide an overview of related works and our research steps, followed by a presentation of ML and DL techniques, and commonly used datasets in this field. Next, a detailed presentation of the current research on IDS based on ML and DL techniques by network categories is provided. The strengths and limitations of ML and DL algorithms, which are frequently used for intrusion detection, are highlighted. Finally, the challenges are discussed and future research directions are proposed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".