Anomaly-Based NIDS Using Artificial Neural Networks Optimised with Cuckoo Search Optimizer

Abstract

Anomaly detection in network traffic is one of the major concerns for the researches and the network administrators. Presence of anomalies in network traffic could indicate a possible intrusion on the network, increasing the need for a fast and reliable network intrusion detection system (NIDS). A novel method of using an artificial neural network (ANN) optimised with Cuckoo Search Optimizer (CSO) is developed in this research paper to act as network monitoring and anomaly detection system. Two subsets of the KDD Cup 99 dataset have been considered to train and test our model, one of 2000 instances and the other of 10,000 instances, along with the complete dataset of 61,593 instances and I have compared the result with the BCS-GA algorithm and the fuzzy K-means clustering algorithm optimised with PSO in terms of precision, recall and f1-score, and the training time for the model with the selected database instances.