A Genetic Algorithm- and t-Test-Based System for DDoS Attack Detection in IoT Networks
Bibliographic record
Abstract
Internet and cloud-based technologies have facilitated the implementation of large-scale Internet of Things (IoT) networks. However, these networks are susceptible to emerging attacks. This paper proposes a novel lightweight system for detecting both high- and low-volume Distributed Denial of Service (DDoS) attacks in IoT networks, namely Genetic Algorithm (GA) and t-Test for DDoS Attack Detection (GADAD). The GADAD system employs edge-based technologies and has three phases. In the first phase, it creates and preprocesses an HL-IoT (High- and Low-volume attacks in IoT networks) dataset, which includes both high- and low-volume DDoS attacks. The second phase introduces a novel and lightweight method, called GAStats, for optimal feature selection using the GA and statistical parameters (Stats.). In the third phase, the system trains three tree-based Machine Learning (ML) models: Random Forest (RF), Extra-Tree (ET), and Adaptive Boosting (AdaBoost), along with other ML models, using both the self-generated HL-IoT dataset and the publicly available ToN-IoT dataset. The evaluation includes the assessment of key performance metrics such as accuracy, precision, recall, F1-score, Receiver Operating Characteristic Curve (ROC), computation time, and scalability analysis with overall system performance. The experimental results illustrate the efficacy of the feature selection method in optimizing the system's efficiency in detecting DDoS attacks in IoT networks, along with a reduction in computation time compared to existing state-of-the-art techniques.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".