Proactive Detection of Attacks on Cloud-based Applications using Machine Learning
Keywords:
Machine Learning, Cloud-Based Applications, Proactive Detection, Cybersecurity, Anomaly Detection, Intrusion DetectionAbstract
This study thoroughly examines how machine learning techniques may be used to proactively detect assaults on cloud-based services. The security of cloud-based systems has become crucial due to the growing dependence on cloud computing for a wide range of applications in many industries. Conventional security methods frequently fail to identify advanced threats that take use of weaknesses in cloud infrastructures and apps. Therefore, there is a critical requirement for security measures that are proactive and adaptable, able to detect and address new threats as they develop instantly. This study focuses on incorporating machine learning methods to enhance the security of cloud-based systems. Our platform utilizes past data on system behaviors, network traffic patterns, and application interactions to use machine learning in distinguishing regular operations from abnormal activity that may signal possible assaults. Our method creates strong detection systems that can adjust to changing threat environments by utilizing feature extraction, dimensionality reduction, and model training. Our technique focuses on creating a comprehensive detection system that includes anomaly detection, intrusion detection, and behavior analysis. Our system is versatile in identifying various assaults such as DDoS attacks, SQL injection, cross-site scripting, and data exfiltration attempts by combining supervised, unsupervised, and semi-supervised learning approaches. Our technique aims to reduce false positives and negatives by detecting discriminative features and reducing noise, thereby enhancing detection accuracy and reliability. The scalability and efficiency of the proposed framework are crucial due to the dynamic and resource-limited nature of cloud infrastructures. We investigate lightweight machine learning techniques and distributed computing architectures that can easily integrate with cloud settings while reducing computational overhead. This study showcases the effectiveness and durability of our proactive detection architecture in protecting cloud-based apps from various cyber threats, through thorough testing and assessment using real-world datasets and simulated attack scenarios. Organizations may reduce the risks of cyber-attacks and protect vital assets, data integrity, and user trust in cloud computing ecosystems by adopting a proactive security approach based on machine learning insights.
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