Intelligent Ensemble based System for Rare Attacks Dectection in IoT Networks
Bibliographic record
Abstract
The lack of security techniques that assures the integrity of data generated by the Internet of Things networks is one of the major stumbling blocks that hinders the whole development of these networks. As smart devices generate and transmit confidential and sensitive data, in these cases, data leakage can lead in many cases to drastic consequences. These types of attacks are called compromised attacks and the U2R and R2L subcategories are typical examples. Despite various intrusion detection systems designed for detecting compromised attacks, many of them are deficient and suboptimum due to the highly sophisticated behavior of these attacks that mimic normal ones, as well as the rarity of occurrence, and lack of training records keep this area inconclusive. Therefore, this paper presents an ensemble-based intrusion detection system to identify rare hard-to-detect attacks (U2R and R2L) in IoT networks. The ensemble is composed of two main tiers. Each tier consists of a major improved KNN classifier driven by multiple auxiliary classical KNN classifiers. the combined responses of these detection tires are fused to provide the optimal detection performance for minority attacks. The experimental analysis unveiled that the proposed system achieved a high detection accuracy reaches up to 96.65% and 100% for R2L and U2R respectively along with Cohen's kappa coefficient reaches up to 0.9778 and 0.996, which confirms the reliability and robustness of the proposed system to be deployed in loT networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".