Predictive Technique Of Security Data Breaches In Ai Powered Mobile Cloud Application Using Deep Random Forest Algorithm
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Abstract
With the rapid integration of artificial intelligence (AI) in mobile cloud applications, ensuring robust security mechanisms is vital to safeguard sensitive user data. The proliferation of AI technologies in mobile cloud applications has brought unprecedented efficiency and convenience, accompanied by an escalating risk of security breaches. As the threat landscape evolves, traditional security measures fall short in providing comprehensive protection. This research recognizes the critical need for a predictive approach to security data breaches in AI-powered mobile cloud applications. Existing security frameworks often lack the adaptability to detect and pre-emptively address emerging threats specific to AI-enhanced mobile cloud environments. This study employs the Deep Random Forest Algorithm, an advanced machine learning technique known for its ability to handle complex and dynamic datasets. The algorithm combines the power of deep learning with the versatility of random forest classifiers to predict security breaches in real-time. The results demonstrate the efficacy of the proposed Deep Random Forest Algorithm in predicting and mitigating security breaches in AI-powered mobile cloud applications. The model exhibits high accuracy and sensitivity, showcasing its potential to enhance the security posture of mobile cloud ecosystems.
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References
Sharma, A., & Singh, U. K. (2022). Modelling of smart risk assessment approach for cloud computing environment using AI & supervised machine learning algorithms. Global Transitions Proceedings, 3(1), 243-250.
Irshad, R. R., Hussain, S., Hussain, I., Alattab, A. A., Yousif, A., Alsaiari, O. A. S., & Ibrahim, E. I. I. (2023). A Novel Artificial Spider Monkey Based Random Forest Hybrid Framework for Monitoring and Predictive Diagnoses of Patients Healthcare. IEEE Access.
Aldhyani, T. H., & Alkahtani, H. (2022). Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments. Sensors, 22(13), 4685.
Yuvaraj, N., Praghash, K., Logeshwaran, J., Peter, G., & Stonier, A. A. (2023). An Artificial Intelligence Based Sustainable Approaches—IoT Systems for Smart Cities. In AI Models for Blockchain-Based Intelligent Networks in IoT Systems: Concepts, Methodologies, Tools, and Applications (pp. 105-120). Cham: Springer International Publishing.
Alkhudaydi, O. A., Krichen, M., & Alghamdi, A. D. (2023). A Deep Learning Methodology for Predicting Cybersecurity Attacks on the Internet of Things. Information, 14(10), 550.
Paul, L. M. F. V., Chooralil, V. S., & Yuvaraj, N. (2022). Modelling of Maximal Connectivity Pattern in Human Brain Networks. NeuroQuantology, 20(6), 4410.
El-Kassabi, H. T., Serhani, M. A., Masud, M. M., Shuaib, K., & Khalil, K. (2023). Deep learning approach to security enforcement in cloud workflow orchestration. Journal of Cloud Computing, 12(1), 10.
Veerappan, K. N. G., Natarajan, Y., Raja, A., Perumal, J., & Kumar, S. J. N. (2023). Categorical Data Clustering using Meta Heuristic Link-Based Ensemble Method: Data Clustering using Soft Computing Techniques. In Dynamics of Swarm Intelligence Health Analysis for the Next Generation (pp. 226-238). IGI Global.
Douiba, M., Benkirane, S., Guezzaz, A., & Azrour, M. (2023). Anomaly detection model based on gradient boosting and decision tree for IoT environments security. Journal of Reliable Intelligent Environments, 9(4), 421-432.
Sangeetha, S. B., Sabitha, R., Dhiyanesh, B., Kiruthiga, G., Yuvaraj, N., & Raja, R. A. (2022). Resource management framework using deep neural networks in multi-cloud environment. Operationalizing Multi-Cloud Environments: Technologies, Tools and Use Cases, 89-104.
Hernandez-Jaimes, M. L., Martinez-Cruz, A., Ramírez-Gutiérrez, K. A., & Feregrino-Uribe, C. (2023). Artificial intelligence for IoMT security: A review of intrusion detection systems, attacks, datasets and Cloud-Fog-Edge architectures. Internet of Things, 100887.
Kousik, N. V., Jayasri, S., Daniel, A., & Rajakumar, P. (2019). A survey on various load balancing algorithm to improve the task scheduling in cloud computing environment. J Adv Res Dyn Control Syst, 11(08), 2397-2406.
Abdullahi, M., Baashar, Y., Alhussian, H., Alwadain, A., Aziz, N., Capretz, L. F., & Abdulkadir, S. J. (2022). Detecting cybersecurity attacks in internet of things using artificial intelligence methods: A systematic literature review. Electronics, 11(2), 198.