Developing an Improved Method for Malware Attack Detection in Cloud Computing using Ensemble Learning

Nowadays, detecting unusual events in the network has been the subject of many researches. Network traffic is huge and very large, and this leads to high data size and increased noise, which makes it very difficult to extract meaningful information to detect abnormal events. Early detection of attacks improves the stability of a system. Each attack is a type of specific behavior; But some attacks may behave similarly and differ only in some features. This article presents a new way to detect malware and attacks in the cloud computing environment. In this method, data clustering separates the data from each other to provide better conditions for model construction by balancing the data in different classes. This research uses a combination of Adabost, Random Forest and Bosted Gradient Tree algorithms as ensemble learning to improve malware detection in cloud computing. In order to combine boosted learners and build a higher level model, the voting mechanism is used. In the proposed model, ensemble learning, using the strengths of various algorithms, creates a useful, high-performance system for detecting malware in cloud computing. By applying the proposed method on real data, it was observed that the accuracy of the proposed method is equal to 99.96%, its accuracy is equal to 99.97% and its recall is equal to 99.95% which compared to previous methods, it has a noticeable advantage, but its computational complexity has not changed much.