Intrusion detection in networks using cuckoo search optimization

One of the key problems for researchers and network managers is anomaly detection in network traffic. Anomalies in network traffic might signal a network intrusion, requiring the use of a quick and dependable network intrusion detection system. Intrusion detection systems based on artificial intelligence (AI) techniques are gaining the interest of the research community as AI techniques have evolved in recent years. This research proposes a novel method for anomaly detection using artificial neural networks (ANNs) optimized using cuckoo search algorithm. For simulation purposes, the NSL-KDD dataset has been utilized with a 70:30 ratio where 70% of data is used for training and the remaining 30% is used for testing. The proposed model is then evaluated in terms of mean absolute error, mean square error, root-mean-square error, and accuracy. The results of the proposed work are compared with standard methods available in the literature including fuzzy clustering artificial neural network (FC-ANN), intrusion detection with artificial bee colony, neural network intrusion detection system, and selection of relevant feature. The results clearly show that the proposed method outperforms the listed standard methods.

Automated summary

The study introduces a novel method for anomaly detection using artificial neural networks and cuckoo search algorithm, and demonstrates its superiority in network intrusion detection through empirical research.