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The risk of sophisticated cyberattacks against real-time services has dramatically grown due to the quick adoption of cloud computing in smart city infrastructures. Due to their high false alarm rates and limited flexibility, traditional security measures frequently fail to identify clever and changing crimes. An intelligent hybrid learning approach for real-time cloud security threat detection in smart cities is proposed in this research. The suggested method combines deep learning and machine learning approaches to improve classification accuracy, identification, temporal attack pattern and feature representation. Standard performance indicators, including as accuracy, precision, recall, F1-score, false alarm rate, and detection time, are used in extensive tests to assess the model's efficiency. According to experimental data, the suggested hybrid model performs better, achieving an accuracy of 96.3% and drastically lowering the false alarm rate to 2.1%. Its viability for real time deployment in cloud systems for smart cities is further confirmed by the decreased detection time. The suggested methodology provides a scalable and dependable way to improve cloud security tolerance.
In order to facilitate massive data processing, real time service delivery, and intelligent decision-making in fields like public safety, healthcare, transportation, and energy management, smart cities are depending more and more on cloud computing infrastructures. The digital foundation of smart urban ecosystems is formed by the constant data collecting and analytics made possible by the integration of Internet of Things (IoT)[1] devices, edge computing, and cloud platforms. However, a variety of cyber security risks, such as distributed denial-of-service (DDoS) attacks, data breaches, malware injection, insider threats, and advanced persistent threats, are also made possible by smart cities' heavy reliance on cloud-based services. These attack have the potential to seriously[2] impair vital services, jeopardize private citizen information, and erode public confidence in smart city technologies. For smart city settings, conventional cloud security techniques like rule-based firewalls and signature based intrusion detection systems are frequently insufficient. Because these techniques are static and have little flexibility, they usually fail to identify sophisticated multi-vector threats and zero-day attacks. Furthermore, real-time threat detection, scalability, and accuracy are subject to strict constraints due to the huge volume, velocity, and heterogeneity[3] of data created by smart city applications. Because of this, there is an increasing demand for intelligent security frameworks that can quickly react to new threats in cloud environments and dynamically understand intricate attack patterns.