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TopIntroduction
Cloud computing (CC) is one of the fastest-growing technologies, revolutionizing data computation and storage across various industries (El Bouanani et al., n.d.). As cloud services become increasingly widespread, the importance of computer and network security has surged due to a rise in attacks targeting data availability, confidentiality, and integrity. These intrusions aim to steal or compromise sensitive data from individuals or organizations, posing significant risks to cloud environments. Despite the implementation of various security measures over the past decade, such as firewalls, user authentication, and access controls, these technologies primarily defend against external threats and are vulnerable to insider attacks (IEEE Communications Society & Institute of Electrical and Electronics Engineers, n.d.). Intrusion Detection Systems (IDSs) are essential to contemporary security systems because they enable a preventative layer of protection. IDSs may dynamically monitor and analyze network traffic, recognizing patterns of aberrant or suspicious activity that can disclose both known and novel assaults, in contrast to more traditional preventative measures like firewalls and antivirus software, which seek to stop known threats (Nandurdikar et al., 2020; Security, 2025). IDSs work by analyzing data from networks or computers, such as packet sizes and characteristics, attacker behavior, and access rules, making them a crucial tool in active security defense. However, current intrusion detection systems encounter obstacles concerning precision, identification of unfamiliar assaults, and mitigation of erroneous alerts as a result of inadequate verification. and testing. Recent studies have explored integrating classical machine learning (ML) and deep learning techniques to improve IDS performance (Z. Ahmad et al., 2021). Machine learning, a key aspect of Artificial Intelligence, automates the creation of analytical models, enabling systems to learn from data and recognize patterns with minimal human intervention (Alotaibi & Rassam, 2023). This research aims to provide a comprehensive review of the current ML techniques, including feature scaling, feature reduction, and oversampling methods. In addition, it determines the most effective combination for enhancing IDS performance and cloud computing security. The study seeks to better distinguish between attacks and legitimate activities in cloud environments.
Motivation
Integrating ML with Intrusion Detection Systems (IDS) enhances the detection and response capabilities against various security breaches. IDS systems that utilize ML can analyze data from multiple sources, improving malware detection and classification, thereby bolstering network security (Attou, Mohy-eddine, et al., 2023). The motivations for this research are:
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Growing Cloud Adoption: As more companies use cloud services, better security measures are urgently needed to safeguard private information and cloud resources from potential threats.
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Traditional IDS Limitations: In cloud environments, traditional IDS solutions often fall short in identifying and evaluating network intrusions. ML techniques are essential for improving the adaptability, scalability, and effectiveness of IDS systems in order to solve this problem.
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Applying ML: IDS's ability to detect and thwart network assaults may be significantly enhanced by utilizing ML techniques such as supervised learning and anomaly detection. This approach aims to improve detection accuracy, get rid of false positives and negatives, and handle new and zero-day attacks efficiently.
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Feature Selection Optimization: Knowing how feature selection and dimensionality reduction impact IDS performance is essential for cloud security. These processes may be enhanced to deliver more effective and efficient IDS operations by concentrating on important factors and simplifying data.