Detecting Network Anomalies using Multilayer Feature Selection Techniques and Machine Learning Algorithms

With the increasing amalgamation of human lives and computer technology we are more dependent on the computer networks. With our enormous dependency of the networks for our daily to day life tasks comes the threats of network attacks. Computer professionals have observed a substantial number of wide-ranging attacks in the recent years. As compared to the previous decade we are much more dependent on computer technology for the crucial day to day tasks such as banking, communication, travelling. Imagining a life without computers and in turn computer networks is not possible. The extensive dependence of human lives on computers has come to the point that the harm that can be caused from the network is no longer only monetary, we have a lot of personal data on the networks that can easily sabotage our life. So, ensuring safety and security over the networks is the need of the hour. Through this research paper we implemented some intrusion detection techniques and we provide a comprehensive study of these techniques. We used Intrusion Detection Evaluation Dataset (CIC-IDS 2017) from Canadian Institute of Cybersecurity.