MLIDS: Machine Learning Enabled Intrusion Detection System for Health Monitoring Framework Using BA-WSN

TL;DR: In this paper , the authors present a taxonomy-based categorization of the proposed solutions for intrusion detection systems (IDSs) in the Internet of Things (IoT).

TL;DR: This study presents Ensemble-Guard IoT, a lightweight ensemble model combining GNB, LR, and RF for real-time IoT attack detection on imbalanced datasets, achieving 99.63% accuracy, 1.00% precision, and 524.40s computation time, outperforming classical schemes.

TL;DR: The pressing need for robust security measures to safeguard patient information is highlighted and the promising role of the ML techniques in addressing these challenges are explored and the contemporary solutions employed for enhancing the security of H-IoT systems during the period spanning from 2020 to 2023 are reviewed.

TL;DR: A specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks and Artificial Neural Network (ANN) has been trained on the dataset to detect and classified different DoS attacks.

TL;DR: This paper has evaluated prediction systems for diseases such as heart diseases, breast cancer, diabetes, spect_heart, thyroid, dermatology, liver disorders and surgical data using a number of input attributes related to that particular disease.

TL;DR: The results showed that the proposed GWOSVM-IDS with seven wolves overwhelms the other proposed and comparative algorithms.