Review on ACO-based Intrusion Detection Method in Computer Networks using Fuzzy Association Rules

The identification of harmful activity and infiltration into systems and computer networks is a fundamental difficulty in this field due to the growing usage of computer networks. Nine fundamental steps make up the study’s suggested approach for intrusion detection using fuzzy association rules and ant colony optimization. Every node in the graph that Ant Colony operates on gives seven parameters for the suggested approach. The Apriori method creates the initial fuzzy rules based on the values in the ant colony’s most populous city in the graph. To increase the precision of intrusion detection in computer networks, the main rules are refined through a process known as conventional weighted aggregation. Using decision trees using NSL-KDD data, the efficiency of the optimized rules is assessed. Assessments show that the suggested approach has a high degree of intrusion detection accuracy and decrease in error risk when compared to other approaches.