The migration to cloud computing has increased exponentially with its measured, available and scalable services, as many companies have subscribed to the IaaS model to freely address their IT needs. However, the security of their data, especially if the cloud is public, remains a major concern for them. Therefore, an intrusion detection system can be developed as a defense mechanism to monitor, detect and prevent threats. In this field, the researchers were interested in the algorithms used in the mechanism of detection, in addition to the location of IDS in cloud computing. These two main points aim to reach high detection accuracy and reduce the false alarms of internal and external, as well as known and unknown intrusions. In this paper, we propose an adaptable solution to IaaS cloud using a hybrid method, which is based on Snort and majority voting technique. Our proposed method provides high detection accuracy and low number of false alarms and confirms its reliability for IaaS cloud.