Deep Unified Model for Intrusion Detection Based on Convolutional Neural Network
Indian army has always been subject to military attacks from neighbouring countries. Despite many surveillance devices and border security forces, the enemy finds a way to infiltrate deep into our borders. This is mainly because even now the surveillance in India is largely human assisted. Therefore this automated surveillance can authenticate the authorized persons and alert everyone when an enemy intrusion is detected. In this, we proposed an automated surveillance system that tackles the predicament of recognition of faces subject to different real time scenarios. This model incorporates a camera that captures the input image, an algorithm to detect a face from the input image, recognize the face using a convolution neural network along with transfer learning method, and verifies the detected person. The authorized person’s name and details are stored in CSV format and then into the database. In case of any unauthorized persons face is detected the image of the intruder along with time is stored in the database and warning signal is also given to alert the surrounding members in case of intrusion detection.
by Dhanu Shree D | Fouzia Fathima A | Madhumita B | Akila G | Thulasiram S “Deep Unified Model for Intrusion Detection Based on Convolutional Neural Network”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021,
URL: https://www.ijtsrd.com/papers/ijtsrd39976.pdf
callforpapermedicalscience, medicalsciencejournal