Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
The research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches.
Abstract
Abstract— This research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest – 95% of overall accuracy as opposed to 92%. It is particularly important to consider AUC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization – features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based
systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.
Keywords- Cloud Data Security, Machine Learning, Random Forest, Deep Neural Networks, Q-Learning
Abstract— This research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest – 95% of overall accuracy as opposed to 92%. It is particularly important to consider AUC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization – features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.
Keywords- Cloud Data Security, Machine Learning, Random Forest, Deep Neural Networks, Q-Learning
Bharda Priya Dutt, Kiran B. M., G. Prasad· International Scientific Jou...· 0 citations
This study takes a close look on how well deep learning models can find cyber threats in Cloud Based Intrusion Detection Systems. Increasing amounts of organizations are using cloud services, and as this number grows many organizations need an effective and resilient way to identify intrusions when cyber threats continue to grow in number and sophistication. Our research aimed to analyze how good different deep learning models are at detecting and classifying cyber-attacks that occur in a cloud-based environment. In order to achieve this objective, we utilized the NSL-KDD dataset, with two deep learning algorithms (Long Short Term Memory LSTM and Conventional Neural Network). Also, we applied a number of different performance measurements (accuracy, recall, precision, F1-score, confusion matrix), in order to determine how applicable they are to realworld intrusion detection systems. The results showed that, LSTM achieved the highest performance, while still having a solid balance between both accuracy and precision/recall. Our results demonstrate the enormous promise of ensemble and gradient boosting techniques in helping to increase the detection capabilities of cloud based IDSs. Our results illustrate the importance of deep learning in increasing the reliability, robustness, and efficiency of intrusion detection systems used in cloud environments, and ultimately contributes significantly to the security of our digital ecosystems.
F. Alsuhimat, Sana Ahmad Iqbal Daien· IEEE Jordan Conference on Ap...· 0 citations
In the context of the widespread deployment of cloud computing and the Internet of Things, enterprise business systems are constantly exposed to the open network environment. Abnormal behavior identification has become a crucial link in the data security protection chain. Based on multi-source security logs and behavior sequence data, a deep learning anomaly identification model integrating embedded representation, bidirectional recurrent networks/Transformer encoding, attention weighting, and adaptive threshold control was constructed. An adversarial perturbation constraint and security interval loss were introduced to form a robust training framework. At the same time, an anomaly behavior interpretation and rule extraction mechanism based on attention weights and gradient contributions was designed to achieve a closed loop from model output to security policy generation. Experiments were conducted using approximately 3.2 million real business log data covering over 8,000 accounts and 1,200 hosts. Compared with rule detection, Isolation Forest, and ordinary sequence models, the proposed method achieved better performance in terms of F1 value (0.90) and AUC (0.94), with a false alarm rate reduced to 0.05. This validates the effectiveness of deep learning technology in improving the accuracy and stability of abnormal behavior identification in data security protection scenarios, providing a method reference for building interpretable and scalable intelligent security protection systems in complex business environments.
Shuai Long, Zeliang Xiao· International Conference on...· 0 citations
Cloud computing settings face growing difficulties in efficient resource use, secure data processing, and proactive detection of threats due to their dynamic and massive nature. Placing and moving virtual machines (VMs) in incorrect locations can waste resources, slow down performance, and raise operating expenses. The present research presents a deep learning--driven secure cloud resource deployment architecture that includes intelligent VM placement and migration, adaptive encryption, and machine learning--based anomaly detection to solve these problems. The suggested approach uses deep learning models to make more accurate decisions on where to place and move virtual machines by looking at real-time workload patterns, resource availability, and system performance metrics. This method uses less energy, lowers latency, and makes better use of resources in all cloud data centers. To keep data secure, adaptive encryption methods based on powerful cryptographic protocols are used dynamically dependent on how sensitive the data is and how it can be accessed. It allows for real-time data protection with minimal extra processing power needed. Additionally, the framework includes machine learning-based models for finding unusual activity in the cloud that are continually functioning. These models look at network traffic, user behaviours, and system logs to find strange patterns and possible security holes. When a vulnerability is found, automated threat response systems are activated to lower risks while maintaining services functioning. Evaluations show that the proposed framework makes VM deployment more efficient, makes data security stronger, and makes anomaly detection more accurate than standard cloud management methods.
Cloud environments, and Amazon Web Services (AWS) in particular, host high-value data assets and mission-critical workloads that make them attractive targets for Advanced Persistent Threat (APT) actors. Because forensic investigation techniques are applied only after a breach has already been discovered, the volume and velocity of cloud-generated telemetry make proactive, automated detection capabilities essential. This paper reviews machine learning-driven anomaly detection paradigms — supervised, unsupervised, semi-supervised, and deep learning — and examines their suitability for APT detection in AWS environments. It also reviews the AWS shared-responsibility security architecture, including Identity and Access Management (IAM), encryption services, and logging and monitoring services such as AWS Cloud Trail, AWS Config, Amazon Guard Duty, Amazon Detective, and Amazon Inspector, and considers the NIST Cyber security Framework (CSF) as a governance overlay that connects these technical capabilities to organizational risk management. Drawing on this review of the peer-reviewed and primary-source literature, the paper argues that no single detection paradigm is likely sufficient on its own, and that unsupervised and semi-supervised machine learning, combined with AWS-native security services and governed by the NIST CSF, offer a more resilient conceptual basis for cloud APT defense than any single method in isolation. On this basis, the paper proposes an Integrated Cloud APT Detection and Defense Model (ICADDM) as a conceptual architecture for researchers and practitioners, maps AWS security services against the MITRE ATT&CK Cloud Matrix, and identifies the empirical validation of the model against real cloud telemetry as the principal direction for future work.
Adeolu Opeyemi Ojo, Samuel Babafemi Olabisi· International Journal of Lat...· 0 citations
As we move further into a more digital supply chain, we have seen tremendous improvements in terms of efficiency, but we have also seen a rise in the risks that data vulnerabilities pose, particularly for intermediary supply chain nodes. As a result, traditional security measures have proven inadequate in protecting critical data that flows through these intermediary supply chain nodes, making them more susceptible to possible security breaches and tampering. In this paper, we introduce a novel solution that utilizes cryptography and Artificial Intelligence (AI)-powered predictive modeling for the security of supply chain data. Unlike other solutions that only protect endpoint data, our solution provides a more comprehensive security solution that extends cryptography for intermediary supply chain data, making it more secure and protected from possible breaches and tampering. To further improve our solution, we have utilized AI models, namely, XGBoost, Random Forest, and LightGBM, for predictive modeling. To prevent target leakage, features arithmetically derived from the recovery-duration target were excluded from the predictor set prior to training. The results indicate that, once the leakage-affected features are removed and hyperparameters are properly tuned, all five models achieve modest but genuine predictive accuracy, with R2 values in the range of 0.542–0.552. The best-performing model on this leakage-audited feature set is XGBoost, with an R2 value of 0.5525 and a test RMSE of 38.16 days, closely followed by Linear Regression and Ridge Regression (RMSE = 38.22 days, R2 = 0.5512), with the practical difference between the two being small (0.06 days RMSE) despite being statistically consistent across ten random splits. This demonstrates the solution’s effectiveness in reducing security risks while maintaining realistic, leakage-free predictive accuracy.
Alfaiz Madhiya, Vijay Solanki, K. Ahmed et al.· Computers· 0 citations
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