Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000

Real-Time DDoS Attack Detection System Using Big Data Approach

Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.

Study on the Spatial Differentiation of the Populations on Both Sides of the “Qinling-Huaihe Line” in China

The “Qinling-Huaihe Line” is the recognized geographical boundary between north and south China. In the context of a widening north–south gap, the large-scale population flow and the implementation of the regional coordinated development strategy, the north–south differentiation of the Chinese population requires further investigation. This study is based on national census data and uses quantitative methods, such as the centralization index, coefficient of variation, hot spot analysis and geodetector, as research methods. This study takes the Qinling-Huaihe Line as the dividing line and aims to extensively explore the spatial differentiation, evolutionary characteristics, and influential factors of the populations on both sides. The main conclusions are as follows: ① From 1982 to 2010, the population share ratio on the south and north sides of the Qinling-Huaihe Line remained at 58:42, showing a distribution pattern of “South more and North less”. ② The area within 200 km from the Qinling-Huaihe Line is a transition area with a stable distribution of the populations on both sides. ③ From 1982 to 2010, the concentration of the population distribution gradually increased on both sides, and the concentration of population on the south side was higher; the characteristics of population growth had significant spatial differences between the two sides. ④ The results calculated by the geodetector method show that socioeconomic factors are the main factors causing the spatial differentiation of the populations, while physical geographical environmental factors have a smaller influence and their influence continues to decrease.