Detection of Network Attacks using Machine Learning: A New Approach
Abstract: The Cyber-attacks become the most important security problems in the today’s world. With the increase in use of computing resources connected to the Internet like computers, mobiles, sensors, IoTs in networks, Big Data, Web Applications/Server, Clouds and other computing resources, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. These intrusions detection techniques have been applied on various IDS datasets. UNSW-NB15 is the latest dataset. This data set contains different modern attack types and wide varieties of real normal activities. In this paper, we compare Naïve Bays algorithm with proposed probability based supervised machine learning algorithms using reduced UNSW NB15 dataset. Keywords: UNSW NB-15, Machine Learning, Naïve Bayes, All to Single (AS) features probability Algorithm