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Due to the rapid advancement of technologies including the tremendous growth of multimedia content, cloud computing and mobile usage, conventional networks are not able to meet the demands. Software-Defined Networks (SDN) are considered one of the key enabling technologies providing a new powerful network architecture that allows the dynamic operation of different services using a common infrastructure. Despite their notable gains, SDNs may not be secure and are vulnerable to attacks. In this paper, we address the SDN vulnerabilities and present attack-specific feature selection to identify the features that have the most impact on anomaly detection. We first use the InSDN intrusion dataset that considers different attacks including Denial-of-Service (DoS), Distributed-DoS (DDoS), brute force, probe, web and botnet attacks. We then perform data pre-processing and apply univariate feature selection to …