Entropy and Divergence-based DDoS Attack Detection System in IoT Networks
Bibliographic record
Abstract
High and low-volume Distributed Denial of Service (DDoS) attacks are critical threats to many Internet of Things (IoT) networks. Low-volume attacks gradually overwhelm the device’s resources, whereas high-volume attacks suddenly flood the device’s resources, causing a decline in Quality of Service (QoS). Researchers have proposed various methods to detect DDoS attacks based on statistical and Machine Learning (ML) approaches. Research has also shown that statistical approaches are more efficient for IoT networks as they are simpler to develop and have better real-time performance. However, most existing ML and statistical-based detection methods are effective for either high-volume or low-volume attacks but not for both. This paper proposes a novel Entropy and Divergence-based DDoS Attack Detection (EDDAD) system that uses a statistical approach to simultaneously detect high and low-volume DDoS attacks with high accuracy. The EDDAD system computes entropy and Kullback-Leibler (KL) divergence of flow features in a time window to detect malicious traffic in IoT networks with adaptive thresholds that utilize statistical information. Our analysis of experimental results from a real testbed demonstrated that the EDDAD system is effective and can achieve detection accuracy of greater than 90% for both high and low-volume DDoS attacks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".