Feature Selection Using a Combination of Ant Colony Optimization and Random Forest Algorithms Applied To Isolation Forest Based Intrusion Detection System

Abstract

For businesses to operate effectively, networks and computer systems have become crucial instruments. They are now used in all professional fields, including the military, universities, banks, and insurance companies: as a result, a computer network enables a variety of tasks to be completed, including the sharing of data between users, the sharing of physical resources like the printer, the use of specific applications without having to install them on one's own computer, and the use of the Internet to conduct information searches, however security issues exist with computer networks. A computer would be more open to all types of attacks if it was connected to a network. Anomaly detection is the process of identifying outliers, or data points that differ significantly from the majority of other data points. In this paper, we present our solution in two chained phases, the first one is the Feature Selection Component using the combination of the two main algorithms ACO and Random Forest for the detection of relevant attributes, and the second phase is the Intrusion Detection Component based on the Isolation Forest algorithm to classify and detect intrusions.