Computer networks anomaly detection by using PCA & pattern recognition - Elham Bideh; Javad Vahidi (Mathematics and Computational Sciences)
The detection of anomalies in computer networks is one of the most considerable challenges that experts in this field are facing nowadays. Thus far, different artificial intelligence methods and algorithms have been proposed, tested, and utilized for detecting these anomalies. However, attempts made to enhance the speed and accuracy of these anomalies’ detection process are continuously ongoing. In this research, pattern recognition based on artificial neural networks is applied to automatically detect anomalies in computer networks. Also, to increase the speed of the pattern recognition based on the process of the neural network, the principal component analysis algorithm will be used as a method for dimension reduction of training samples. The results of the performed simulations based on the proposed methods in this research show that dimension reduction of training samples by principal component analysis algorithm and then applying the pattern recognition based on neural networks method leads to high-speed (less than 10 seconds) and high-accuracy (99-100%) detection of anomalies in computer networks.