A Technique for Generating a Botnet Dataset for Anomalous Activity Detection in IoT Networks
Bibliographic record
Abstract
In recent times, the number of Internet of Things (IoT) devices and the applications developed for these devices has increased; as a result, these IoT devices are targeted by many malicious activities that cause potential damage in many smart infrastructures. A technique is required to appropriately classify anomalous activities to minimize the impact of these activities. The IoT networks are difficult to analyze and test because of the lack of sufficient well-structured IoT datasets for anomaly-based intrusion detection. In this paper, we present a technique we have used to generate a new Botnet dataset, from an existing one, for anomalous activity detection in IoT networks. The new IoT botnet dataset has a wider network and flow-based features. A flow-based Intrusion Detection System (IDS) can be analyzed and tested using flow-based features. Finally, we use different machine learning methods to test the accuracy of our proposed dataset. We also test the accuracy of our proposed dataset through various feature correlation and the methodology for recursive feature elimination. Our proposed IoT botnet dataset provides a ground to analyze and evaluate anomalous activity detection model for IoT networks. We have shared the newly generated Botnet dataset publicly, and a link is provided in this paper.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".