New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models (such as Logistic and Hill equations) with the real data as reference. Furthermore, this work uses the goodness of fitting to evaluate the fitting effect of the improvement. Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect. Compared with the previous forecasting methods, the contributions of our proposed improvement methods are mainly in the following aspects: 1) we have fully considered the spatiotemporal characteristics of the data, rather than single standardized data. 2) the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting. 3) we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.
Distributed denial-of-service (DDoS) has caused major damage to cloud computing, and the false- and missing-alarm rates of existing DDoS attack-detection methods are relatively high in cloud environment. In this paper, we propose a DDoS attack-detection method with enhanced random forest (RF) optimized by genetic algorithm based on flow correlation degree (FCD) feature. We define the FCD feature according to the asymmetric and semidirectivity interaction characteristics and use the two-tuples FCD feature consisting of packet-statistical degree (PSD) and semidirectivity interaction abnormality (SDIA) to describe the features of attack flow and normal flow. Then we use a genetic algorithm based on the FCD feature sequences to optimize two key parameters of the decision tree in the RF: the maximum number of decision trees and the maximum depth of every single decision tree. We apply the trained RF model with optimized parameters to generate the classifier to be used for DDoS attack-detection. The experiment shows that the proposed method can effectively detect DDoS attacks in cloud environment with a higher accuracy rate and lower false- and missing-alarm rates compared to existing DDoS attack-detection methods.
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