| dc.description.abstract | The increasing complexity of modern networks and the rise of sophisticated cyber attacks has made the development of effective Intrusion Detection Systems (IDS) a critical need. Software De fined Networking (SDN) technology provides us with a programmable central controller, providing a central view of the whole network as opposed to the existing internet structure where each of the routers only has information about it's surrounding routers, which results in the systems and algorithms developed in it to operate
in an distributed setting. The centralized view provided by SDN makes it an attractive platform for IDS deployment. The networks under SDN is, however, more vulnerable to malicious activities or attacks than the traditional network topology due to the same centralised nature. The recently published "inSDN" dataset was prepared specifically for intrusion detection in SDN. In this study, we have used this dataset to introduce a novel Intrusion Detection System (IDS) model that integrates Principal Component Analysis (PCA) - a feature selection methodology commonly used in traditional Machine Learning (ML) to extract the principal features from large datasets and reduce dimensionality - and Artificial Neural Networks (ANN) to classify network tra c based on the extracted features. The
model achieved an accuracy of 99.95% for multi-class classifi cation. The results show that the proposed model outperforms the current state-of-the-art techniques in a much simpler settings and reduces the need for complex models that require extensive computation in the "inSDN" attack dataset. | en_US |
