Ensemble-based Multi-Filter Feature Selection Method for DDoS Detection in Cloud Computing

TL;DR

This paper introduces an ensemble multi-filter feature selection approach that improves DDoS detection in cloud computing by reducing features and enhancing classification accuracy using machine learning.

Contribution

It presents a novel ensemble-based multi-filter feature selection method that outperforms existing techniques in DDoS detection accuracy and efficiency.

Findings

Reduced features from 41 to 13 with high detection rate

Achieved better classification accuracy than other methods

Effective in identifying important features for DDoS detection

Abstract

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.