Securing Networks: A Machine Learning Approach to Intrusion Detection Systems

Our dependency on the Internet for daily work has dramatically increased as the globe observes the rapid improvements in technology. We are increasingly vulnerable to cyberattacks as a result of our increased dependence, and attackers target us more frequently. Our exposure to security attacks increases as our reliance on the Internet grows. As a result, there is an increasing need for products like antivirus software and other systems that can spot new security concerns. It is necessary to find a hardware or software solution that, when included in a network, effectively detects and blocks all forms of intrusions to solve this problem. This technology, also known as an intrusion detection system (IDS), is extremely useful for keeping track of network activity and identifying irregularities. Current studies in this area—Current research in this area attempts to create systems that, in addition to precisely identifying faults, also take proactive measures to eliminate dangers as soon as they are discovered. Using the UNSW-NB15 dataset and Convolutional Neural Network (CNN) technology, this study introduces an improved intrusion detection system (IDS) that aims to identify intrusions more precisely and effectively. The existing IDS's ability to keep up with developing intrusion strategies is limited since it relies on the KDD99 dataset. A fresh dataset that includes a wider variety of attack types and network situations is used as a result.