Classifying network attack types with machine learning approach

The growing rate of network attacks including hacker, cracker, and criminal enterprises have been increasing, which impact to the availability, confidentiality, and integrity of critical information data. In this paper, we propose a network-based Intrusion Detection and Classification System (IDCS) using well-known machine learning technique to classify an online network data that is preprocessed to have only 12 features. The number of features affects to the detection speed and resource consumption. Unlike other intrusion detection approaches where a few attack types are classified, our IDCS can classify normal network activities and identify 17 different attack types. Hence, our detection and classification approach can greatly reduce time to diagnose and prevent the network attacks.