MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review.

Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources. With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs. [ABSTRACT FROM AUTHOR]