Attack Detection From Network Traffic using Machine Learning

Network Security Management is not only becoming difficult but also becoming impossible as size of networks grow. Attacks grow beyond the current ability of security related management tools to identify the attacks and respond quickly. So a machine learning based model is designed to detect most recent and up to date attacks from network flow data of network devices, i.e. network switches, routers, wireless routers, firewalls, etc. Extreme Gradient Boosting based model is designed for attack detection that provides 91.61% detection rate, generate very few false alarms at rate of 0.005% and misses attacks at 8.38% rate over CICIDS dataset which recent open source dataset containing network flow information of network devices