Detecting Botnet Attacks in IoT Environments: An Optimized Machine\n Learning Approach
Bibliographic record
Abstract
The increased reliance on the Internet and the corresponding surge in\nconnectivity demand has led to a significant growth in Internet-of-Things (IoT)\ndevices. The continued deployment of IoT devices has in turn led to an increase\nin network attacks due to the larger number of potential attack surfaces as\nillustrated by the recent reports that IoT malware attacks increased by 215.7%\nfrom 10.3 million in 2017 to 32.7 million in 2018. This illustrates the\nincreased vulnerability and susceptibility of IoT devices and networks.\nTherefore, there is a need for proper effective and efficient attack detection\nand mitigation techniques in such environments. Machine learning (ML) has\nemerged as one potential solution due to the abundance of data generated and\navailable for IoT devices and networks. Hence, they have significant potential\nto be adopted for intrusion detection for IoT environments. To that end, this\npaper proposes an optimized ML-based framework consisting of a combination of\nBayesian optimization Gaussian Process (BO-GP) algorithm and decision tree (DT)\nclassification model to detect attacks on IoT devices in an effective and\nefficient manner. The performance of the proposed framework is evaluated using\nthe Bot-IoT-2018 dataset. Experimental results show that the proposed optimized\nframework has a high detection accuracy, precision, recall, and F-score,\nhighlighting its effectiveness and robustness for the detection of botnet\nattacks in IoT environments.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".