Malicious Network Traffic Detection using Machine Learning

Abstract

The number of linked gadgets in our homes is increasing because of the technological revolution, and cybersecurity must become one of our society’s most pressing concerns. Most organizations are aware of this, and they have entire departments dedicated to ensuring the security of their networks, devices, and products. Furthermore, a growing number of professional solutions aimed at protecting their defenses, such as antivirus, firewalls, and Intrusion Detection and Prevention Systems, are available. When it comes to our own houses, however, cybersecurity is constantly a step behind and has become one of our unfinished responsibilities. This project seeks to create a Network traffic detection, a network intrusion detection system capable of detecting attacks inside a local network and providing a user-friendly interface for monitoring. A Machine Learning model is used to detect malicious traffic. It offers a control interface designed to allow the user to react to any attack and can be taught using data from the same network where it will be deployed.