Enhanced Security: Combining ANN and SVM for Superior Brute Force Attack Detection in Cloud Environments

Protection of private data and resources in cloud environments depends on the prevention and detecting of brute force assaults. Brute force attacks—which include continuously attempting various user names, passwords, or encryption keys—are dangerous as they exploit security flaws in security systems, therefore compromising the stability of the system overall and the protection of sensitive data. Keeping secure digital environments and preventing undesired access depend on effective detection and prevention of brute force assaults. Using the advantages of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), the proposed approach detects and neutralizes any raw force assaults. First, the model searches for plausible instances of brute force assault using SVM's fundamental screening ability. Then, via ANN tweaking, the model's accuracy improves further. With an incredible 82% success rate, the proposed approach looks and enhances outcomes on many datasets throughout a continuous process. The model effectively detects and stops brute force attacks in dynamic security environments by using the finest aspects of SVM and ANN. This robust protection system guarantees that critical systems and data are secure against always shifting attack strategies. The findings underline the need of using innovative machine learning techniques to effectively reduce hacking threats. Furthermore, the current approach is a reasonable alternative for companies trying to increase their defenses against direct force assaults and has significant potential in useful applications. Maintaining the great accuracy of the SVM-ANN architecture and reacting to new threats need for ongoing monitoring and development. By providing practical techniques for safeguarding digital assets, preventing brute force assaults, and maintaining data integrity in cloud environments, this study considerably expands the field of cybersecurity.