Cloud computing on the Internet of Things (IoT) emerged as a revolutionary paradigm, profoundly influencing a myriad of fields, including healthcare systems, military applications, education, and beyond ^[1][2][1,2]. Its allure originated from its inherent cost-efficiency and remarkable reliability, which allowed organizations to scale their operations with unprecedented flexibility. However, with the increasing reliance on cloud infrastructure, there emerged an ominous and ever-present threat of cyberattacks ^[3][4][5][3,4,5]. These nefarious assaults on digital infrastructure disrupt normal system operations, perpetrating malicious activities that compromise data integrity, confidentiality, availability, and privacy.

In response to this growing menace, the imperative to fortify the security of cloud networks has become paramount. Recognizing the urgency of safeguarding these systems against cyberattacks, the concept of the intrusion detection system (IDS) was conceived ^[6][7][6,7]. The role of an IDS is pivotal; it exists to identify and thwart network intrusions, serving as the vigilant guardian of cloud systems. An IDS shoulders the responsibility of not only repelling cyber threats but also upholding the integrity, confidentiality, availability, and privacy of cloud-based operations ^[8][9][8,9].

In the annals of cybersecurity, various soft-computing approaches have been devised as part of conventional efforts to establish effective IDS frameworks [10]. IDS systems typically fall into two main categories: those employing anomaly-based detection and those relying on signature-based detection methods to facilitate trust in communication within cloud networks [11]. Among the arsenal of security models, those rooted in artificial intelligence (AI) have gained prominence as they exhibit a propensity for delivering precise IDS capabilities. Recent research endeavors have underscored the preeminence of machine learning-based classification techniques for the prediction of network breaches. Integral to the development of IDS frameworks are optimization approaches ^[12][13][14][12,13,14], which serve as guiding lights in the selection of pertinent features for training and testing the classifier. For applications related to the prediction and detection of network intrusions, a spectrum of machine learning models exists, encompassing supervised, semi-supervised, and unsupervised techniques ^[15][16][17][15,16,17]. While these methods have exhibited efficacy, they are not without their shortcomings ^[18][19][20][18,19,20]. Common issues include the complexity of comprehension, limitations in handling massive datasets, protracted processing times, substantial storage requirements, and elevated error rates.

In light of these challenges, the focus of this research endeavor is to promote the development of a streamlined, user-friendly strategy to secure cloud systems from cyber threats. By harnessing the power of innovation, the aim is to bridge the gap between the burgeoning complexity of modern cyber threats and the need for efficient, comprehensible, and effective security solutions for cloud-based ecosystems. 

2. Machine Learning (ML) Methods

Machine learning (ML) methods have significantly contributed to the enhancement of cloud security by enabling the detection and mitigation of various cyber threats and vulnerabilities. These ML techniques encompass a wide array of approaches, each tailored to address specific security challenges. However, it is important to note that while ML offers many advantages, it also comes with certain limitations and challenges that need to be considered in cloud security applications. Supervised learning methods, such as support vector machines (SVM) and random forest (RF), have been extensively used for intrusion detection and classification in cloud systems. SVM seeks to find the optimal hyperplane to separate normal from malicious activities, while RF leverages ensemble learning to improve detection accuracy. However, supervised methods heavily rely on labeled training data, which can be scarce and may not adequately represent the evolving nature of cyber threats in the cloud. Additionally, the accuracy of these models can be compromised when faced with adversarial attacks designed to deceive them. Unsupervised learning techniques, including K-Means clustering and DBSCAN, are valuable for identifying anomalies and patterns in cloud network traffic without the need for labeled data. They can uncover unusual behavior that may indicate security breaches. Nevertheless, these methods can produce false positives or miss subtle threats, and they often require careful tuning of hyperparameters to achieve optimal results. Scaling these techniques to handle large and complex cloud environments can also be computationally intensive. Deep learning methods, such as convolutional neural networks (CNN), long short-term memory (LSTM) networks, and autoencoders, have shown promise in cloud security due to their ability to process sequential and high-dimensional data. CNNs are effective at analyzing network traffic patterns, while LSTMs excel in time-series data analysis. Autoencoders are used for anomaly detection by learning to reconstruct normal data patterns. However, deep learning models are data-hungry and require substantial computational resources for training, making them less suitable for organizations with limited data or computational capabilities. They also tend to be opaque, making it challenging to interpret their decision-making processes. Ensemble methods, such as gradient boosting and stacking, improve detection accuracy by combining multiple machine learning models. While these approaches generally yield better results, they can be computationally expensive and may require extensive feature engineering to be effective. Feature selection and engineering techniques, like PCA and RFE, are employed to identify relevant features and reduce dimensionality in cloud security datasets. However, selecting the right features and transforming them appropriately can be a time-consuming and manual process. Hybrid approaches, which combine ML methods with optimization algorithms, aim to improve both detection accuracy and efficiency. These approaches can be highly effective but may require expertise in multiple domains and can be complex to implement and maintain. Reinforcement learning, although less common in cloud security, offers the potential for developing adaptive systems capable of making real-time decisions in response to evolving threats. However, it requires substantial training and may not be well-suited to all cloud security scenarios. Bayesian methods, including Bayesian networks and classifiers, provide a probabilistic framework for modeling relationships in cloud security data. They aid in threat identification and risk assessment by considering uncertainty. Nonetheless, Bayesian models can become computationally expensive as the complexity of the network increases, and they may not always capture complex, nonlinear relationships effectively. In brief, machine learning (ML) methods have revolutionized cloud security by enabling automated threat detection and mitigation. However, they are not without limitations, including the need for labeled data, potential susceptibility to adversarial attacks, computational demands, and challenges related to model interpretability. The choice of ML approach should be carefully considered based on the specific security task, available data, and computational resources, and often a combination of methods is required to achieve robust cloud security. As cloud security threats continue to evolve, ongoing research and innovation in ML techniques will be essential to stay ahead of cyber adversaries.