Cognitive Secure Shield - A Machine Learning Enabled Threat Shield for Resource Constrained IoT Devices
Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018
The Internet of things (IoT) devices come in various operating form factors. Some are operated on unconstrained resources by directly connecting to the electrical grid with Cloud Compute driven memory and processing capacities; others, operated on constrained resources by connecting to finite battery sources and limited memory and compute. Whatever the form factors are, importantly, the expectations from consumers are the IoT devices must be secured - both in terms of data and in terms of safety and efficiency. For securing IoT devices with unconstrained resources, there are many tools and compute technologies are available. On the other hand, Securing IoT devices with constrained resources, the options are few and pose huge challenges in terms of price, performance, and service costs. In this research paper, we propose machine learning enabled cognitive secure shield that secures the Dairy IoT devices operating under constrained resources. Our innovation is in the design of Secure shield framework that enhances security posture of our Dairy IoT device without affecting Useful Life of the device (ULD). Finally, the paper presents Secure shield ML prototyping.
Cow-necklace, Dairy-IoT-Sensor, Edge-Device, Hanumayamma-Innovations-and-Technologies, Kalman-Filter, Layers, Machine-Learning, Secure-Shield, Six-Sigma, Threat-Vectors
Jaya Shankar Vuppalapati, Santosh Kedari, Anitha Ilapakurti, Chandrasekar Vuppalapati, Chitanshu Chauhan, Vanaja Mamidi, and Surbhi Rautji. "Cognitive Secure Shield - A Machine Learning Enabled Threat Shield for Resource Constrained IoT Devices" Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018 (2019): 1073-1080. https://doi.org/10.1109/ICMLA.2018.00175