Network Intrusion Detection for IoT-Botnet Attacks Using ML Algorithms
Bibliographic record
Abstract
Amid the expanding landscape of IoT-Botnet attacks, this research delves into the augmentation of Network Intrusion Detection (NID) through the utilization of Machine Learning (ML) algorithms. Beginning with an exploration of NID's vital role in cybersecurity, this study embarks on a comprehensive investigation. Within a controlled IoT lab, in Canadian Institute for Cybersecurity (CIC), both legitimate and malicious traffic data are meticulously captured and subjected to thorough analysis. Simulated attacks, executed via Kali Linux tools, generate datasets converted into csv formats for systematic examination. The devised methodology encompasses an array of stages, including meticulous data pre-processing, effective partitioning, prudent feature scaling, and the rigorous assessment of diverse ML algorithms-namely, Decision Tree, Random Forest, Logistic Regression, KNeighbors Classifier, and Gaussian Naïve Bayes. Through meticulous training and meticulous testing, the Decision Tree Classifier emerges as a standout performer, demonstrating an impressive accuracy rate of 99.17%. Close behind, the Random Forest Classifier exhibits a commendable accuracy of 99.11%, while the KNeighbors Classifier achieves a noteworthy 98.22% accuracy. These outcomes underline the profound potential of ML algorithms in not only identifying but also effectively countering the escalating challenges posed by IoT-Botnet attacks. The cumulative results of this study contribute substantially to the reinforcement of IoT network security, effectively safeguarding against the ever-evolving landscape of threats.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".