MUMBAI, India, July 13 -- Intellectual Property India has published a patent application (202641081948 A) filed by Malla Reddy Engineering College Women Autonomous; Malla Reddy University; Malla Reddy Mr Deemed To Be University; and Malla Reddy Vishwavidyapeeth Deemed on July 01, 2026, for Mcad- A Machine Learning Based Cyberattacks Detector In Software-Defined Networking (sdn) For Healthcare Systems.
Inventors include Dr. Y. Madhaveelatha; Ms. Rashmi Cigiri; Ms Tejasri Nimmagari; Ms. Nadar Ponraj Sudalamani; Ms. T Vyshnavi; Dr. Lalband Neelu; Ms. Santoshi Chandrayya Itkal; and Mrs. Sai Kumari Thakur.
The application for the patent was published on July 10, 2026, under issue no. 28/2026.
Abstract: The healthcare industry handles sensitive and important data that must be protected from unauthorized access. Software-defined networks (SDNs) are extensively implemented in healthcare systems to assure optimal resource utilization, security, network administration, and control. Due to the sensitivity of patient data, SDNs are exposed to a wide spectrum of intrusions despite their many benefits. These attacks harm the overall network performance and can lead to network failures that pose a risk to human lives. Therefore, we aim to propose a machine learning-based cyber-attack detector (MCAD) for healthcare systems, by adapting a layer three (L3) learning switch application to collect normal and abnormal traffic, and then deploy MCAD on the Ryu controller. Our findings are beneficial for enhancing the security of healthcare applications by mitigating the impact of cyberattacks. This work covers the testing of MCAD using a wide spectrum of both ML algorithms and attacks, and provides a performance comparison for every pair of ML algorithms/attacks to illustrate the strengths and weaknesses of different algorithms against a specific attack. The MCAD shows impressive performance, achieving a good F1-score on normal and attack classes, respectively, which implies a high level of reliability. MCAD also achieved 5,709,692 samples per second on throughput, which reflects a high-performance realtime system with respect to complexity.
Disclaimer: Curated by HT Syndication.
