Enhancing Underwater Network Security: ML-Based Detection and Prediction of DDoS Attacks in IoUT Networks
Bibliographic record
Abstract
The rapid expansion of underwater communication networks through autonomous drones and sensors marks a significant advancement in ocean exploration and data collection. However, this growth introduces new vulnerabilities, particularly to DDoS attacks, jeopardizing the integrity of IoUT (Internet of Underwater Things)devices. Effective mitigation of these attacks requires tailored defence strategies suited to submerged environments. This study provides quality IoUT datasets and presents a predictive model leveraging machine learning techniques to detect and mitigate DDoS threats in underwater networks. By analyzing network traffic, we employ classifiers such as Decision Trees, AdaBoost, SVM, K-Nearest Neighbours, and Random Forests to enhance detection accuracy and response times. The enhanced machine learning result was provided for various conditions of the underwater network (base, light, medium, heavy, and extreme conditions). Compared with prior IoT and IoUT studies, in a terrestrial environment, the accuracy is reported as 99%. The model maintained 99% in base condition and 96% for both validation and test accuracy at 1 meter depth. To validate the models, real-world aquatic environment datasets are collected from various locations and depths. In addition, a data validation model was implemented to verify the quality of the dataset. The proposed solution aims to strengthen the security framework of IoUT devices, ensuring the protection of critical underwater data from emerging cyber threats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".