  Increasing interest in the adoption of cloud computing has exposed it to
cyber-attacks. One of such is distributed denial of service (DDoS) attack that
targets cloud bandwidth, services and resources to make it unavailable to both
the cloud providers and users. Due to the magnitude of traffic that needs to be
processed, data mining and machine learning classification algorithms have been
proposed to classify normal packets from an anomaly. Feature selection has also
been identified as a pre-processing phase in cloud DDoS attack defence that can
potentially increase classification accuracy and reduce computational
complexity by identifying important features from the original dataset, during
supervised learning. In this work, we propose an ensemble-based multi-filter
feature selection method that combines the output of four filter methods to
achieve an optimum selection. An extensive experimental evaluation of our
proposed method was performed using intrusion detection benchmark dataset,
NSL-KDD and decision tree classifier. The result obtained shows that our
proposed method effectively reduced the number of features from 41 to 13 and
has a high detection rate and classification accuracy when compared to other
classification techniques.
