A Hybrid Ensemble Learning-Based Intrusion Detection System for the Internet of Things
Bibliographic record
Abstract
The applications of the Internet of Things (IoT) have grown significantly both in scope and complexity. IoT devices are becoming an integral part of our daily lives. This significant growth in IoT adoption is accompanied by a substantial increase in the interest of malicious actors. IoT devices are a preferred target for malicious actors due to their inherent vulnerabilities and limited computational resources, which make them difficult to protect and secure. This study introduces a novel ensemble learning-based intrusion detection system (IDS) using network flow features. The goal of the proposed system is to achieve both simplicity and high detection accuracy. The novelty behind the system lies in using a new feature called “history”, extracted from flow information, combined with traditional features. The core classification engine includes bidirectional long short-term memory (BiLSTM) and multilayer perceptron (MLP) classifiers, with a decision tree (DT) classifier finalizing the decision-making process. The proposed system has been evaluated using a public IoT network dataset with an accomplished accuracy of 99.6%. The system has achieved results comparable to those of other systems that are more complex. The obtained results demonstrate the superior performance of the proposed ensemble learning-based system in comparison to conventional network-flow-based intrusion detection systems.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".