Abstract: People from all over the world can connect over the Internet. Network attacks are a risk in this Internet environment. The risk of integrity and confidentiality has risen in tandem with the density of information and its global reach. Breach of security has gotten way too regular. As a result, network security is becoming increasingly important these days. Accidental network interference can be avoided by using network protection. It’s made up of network intrusion detection software that monitors the activity on the network. To track traffic from source to destination apps, NIDS is strategically placed throughout the network. The computer would do its best to screen both inbound and outbound traffic, but this would cause traffic congestion, decreasing the system's overall performance. Machine learning approaches such as logistic regression, Naive Bayes, K-Nearest Neighbour, and Decision Trees were applied in the domain of intrusion detection for our research.

Keywords: Network intrusion, Machine learning, Host Intrusion Detection System, Network security

| DOI: 10.17148/IARJSET.2022.9592