Multidimensional Intrusion Detection System for Containerized Environments
Bibliographic record
Abstract
Intrusion Detection Systems (IDS) are critical for securing modern networks and systems; however, traditional IDS approaches often rely solely on network traffic or host-level data, limiting their ability to detect sophisticated threats such as AI-driven, zero-day, and polymorphic attacks. This limitation is even more pronounced in highly dynamic environments, such as cloud-based and containerized architectures, where the potential of leveraging rich contextual information remains underexplored. To address this gap, we propose a novel Multidimensional Intrusion Detection System (MIDS) approach that integrates multiple data dimensions, including network and container features, to enhance threat detection in containerized environments. By combining these dimensions, MIDS provides a holistic view of the cluster, enabling more comprehensive threat analysis and improved detection accuracy. We introduce a new data merging technique that unifies network flows with container metrics to facilitate multidimensional analysis. Due to the lack of existing datasets containing such heterogeneous data, we generated two MIDS datasets by simulating prevalent attacks on two well-known containerized applications deployed on Kubernetes (K8s): one using the Damn Vulnerable Web Application (DVWA) and the other using Google's Bank of Anthos (BoA). These simulations included Denial of Service (DoS), brute force, and SQL injection attacks. We evaluated state-of-the-art machine learning (ML) algorithms on these datasets, including SVM, XGBoost, and DNN. The experimental results demonstrate that using MIDS enables ML algorithms to achieve up to 8.69 % and 30.07 % higher F1 scores compared to using only network or container data, respectively. Feature analysis highlights the complementary contributions of network and container dimensions, showcasing the effectiveness of the proposed multidimensional approach for intrusion detection in containerized environments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".