Cloud computing services have become essential in people’s daily lives. A wide range of organizations, including small businesses, huge corporations, individuals, and government agencies, carry out a significant portion of their operations using cloud services. It has empowered customers to access companies’ services instantly and at the most affordable price, regardless of location or time, using the Internet. Although cloud networks offer various benefits, they are susceptible to numerous forms of assaults. Nevertheless, as the utilization of cloud services intensifies, the vulnerabilities linked to these services have correspondingly risen. To enhance cloud security, many measures have been taken, including the implementation of network monitoring to safeguard the cloud infrastructure and the development of techniques to identify and categorize intrusions. Consequently, an intrusion detection system (IDS) is a crucial safeguard for identifying attacks in the cloud computing network. Existing intrusion detection systems (IDSs) face difficulties in effectively managing and analyzing the vast amount of network traffic present in cloud environments. This hampers the precision of cyberattack detection. This paper aims to provide insight to researchers working in the domain of cyberattack classification in the cloud environment.