Application of continuous threat detection based on computer thermal energy consumption optimization in enterprise cloud finance platform

With the increasing popularity of enterprise cloud financial platforms, cyber security threats are becoming increasingly complex and persistent. Traditional security defense methods are difficult to effectively deal with these new types of threats. Large-scale threat detection systems consume a huge amount of computing resources, which in turn leads to significant thermal energy emissions, posing challenges to the environment and operational costs. This study aims to propose a continuous threat detection method based on computer thermal energy consumption optimization, which is applied to the enterprise cloud financial platform to enhance security defense capabilities and reduce energy consumption. This paper proposes a continuous threat detection method based on computational neural networks. By optimizing the neural network algorithm, its computational complexity is reduced, thereby reducing the thermal energy consumption in the computational process. This method designs a continuous threat detection algorithm, which is capable of monitoring potential threats in the cloud financial platform in real time. The experimental results show that the proposed continuous threat detection method effectively reduces the thermal energy consumption of the computer on the premise of ensuring the detection accuracy. Compared with traditional threat detection methods, this method significantly reduces thermal energy consumption under the same detection performance. In the practical application of the enterprise cloud financial platform, this method can promptly detect and respond to various security threats, significantly enhancing the security of the platform. Therefore, the continuous threat detection method based on computer thermal energy consumption optimization proposed in this paper can effectively enhance the security of the enterprise cloud financial platform and reduce energy consumption at the same time.