A Lightweight and Optimal Defense System for DDoS Attacks in IoMT Networks
Bibliographic record
Abstract
Integrating the Internet of Things (IoT) into the healthcare sector through the Internet of Medical Things (IoMT) has significantly enhanced patient care and the functionality of medical devices. However, this integration has introduced new challenges in cybersecurity, especially in detecting Distributed Denial of Service (DDoS) attacks. While various Machine Learning (ML)-based methods have been proposed to detect DDoS attacks, they face difficulty detecting both high-and low-volume DDoS attacks simultaneously. Additionally, there is a need to identify the optimal defense strategy to safeguard IoMT networks. This paper introduces a Lightweight And Optimal Defense System (LAMDA) for IoMT networks using a novel and efficient feature selection method called Threshold Feature Selection (TFS) with tree-based ML models. The system incorporates a game theory approach to identify the most effective defense strategies, enabling rapid and accurate decision-making during cyberattacks. The performance of the LAMDA system is evaluated using various datasets containing both high-and low-volume DDoS attacks. Results indicate that the LAMDA system, mainly when using the Random Forest model, achieves an accuracy rate of over 93% in detecting such 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".