The Iot-Machine Learning Security Algorithm for Detecting the Intruders Gaining an Unauthorised
The essentiality in the protection of the government restricted areas using the technology of IOT (Internet of Things) has been observed in this research, with the sole aim of providing certain measures to curbing the activities of the terrorists creating dirty scenario within the environment. The neural network employed four input neurons which are the Sensors used as IP address, while the government authorized areas are the clients who receive messages from the IP neurons, there are two separate hidden layers of orders seven each as the processors preceding the output which is the threshold value that has been determined through the sigmoid activation function. The research adopted the deep learning machine language and internet base IP with python socket command lines to address the problem of detecting unauthorized access in the government restricted areas. The unsupervised neural network algorithm used is of configuration 5-7-7-4, which was coded in python functional programming language and trained with the back propagation algorithm with 300 epoch runs to ensure that errors are maintained at about 5% confidence level through the sigmoid activation function. The research concluded that IOT technology if properly annexed is faster and better than conventional security method of narrower view, coverage and limitation to capture intruders invading government protected areas.
