Smart Attack Detection in Medical IoT Using Optimized CNN  and Feature Selection

Hari Suresh Babu Gummadi

Smart Attack Detection in Medical IoT Using Optimized CNN and Feature Selection

Authors

Hari Suresh Babu Gummadi

Keywords:

Internet of Medical Things, Cybersecurity, Slime Mold Algorithm, Sperm Whale Optimization, Convolutional Neural Networks, Anomaly Detection

Abstract

In modern healthcare systems, devices and sensors connected through the Internet of MedicalThings (IoMT) play a vital role by enabling both remote and on-site monitoring of patients’health conditions. These technologies support timely interventions by alerting medical
professionals during emergencies. However, the increasing reliance on IoMT also brings significant cybersecurity challenges

References

Alrawais, A., Alhothaily, A., Hu, C., & Cheng, X. (2017). Fog computing for the Internet of Things: Security and privacy issues. IEEE Internet Computing, 21(2), 3442. https://doi.org/10.1109/MIC.2017.37

Abduvaliyev, A., Pathan, A.-S. K., Zhou, J., Roman, R., & Wong, W.-C. (2013). On the vital areas of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(3), 1223–1232. https://doi.org/10.1109/SURV.2012.121912.00006

Downloads

Published

2023-12-11

How to Cite

Hari Suresh Babu Gummadi. (2023). Smart Attack Detection in Medical IoT Using Optimized CNN and Feature Selection . Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1233–1242. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2547