On data mining applications in mobile networking and network security
Mikhail Zolotukhin · Jyväskylä University Digital Archive (University of Jyväskylä) · 2014
This work focuses on the application of different methods and algorithms of data mining to various problems encountered in mobile networks and computer systems. Data mining is the process of analysis of a dataset in order to extract knowledge patterns and construct a model for further use based on these patterns. This process involves three main phases: data preprocessing, data analysis and validation of the obtained model. All these phases are discussed in this study. The most important steps of each phase are presented and several methods of their implementation are described. In addition, several case studies devoted to different problems in the field of computer science are presented in the dissertation. Each of these studies employs one or more data mining techniques to solve a posed problem. Firstly, optimal positions of relay stations in WiMAX multihop networks are calculated with the help of genetic algorithm. Next, the prediction of the next mobile user location is carried out based on the analysis of spatial-temporal trajectories and application of several classifying methods. After that, the use of clustering and anomaly detection techniques for the detection of anomalous HTTP requests is presented. Finally, the data mining approach is applied for the detection and classification of malicious software. These case studies show that data mining methods can help to solve many different problems related to mobile networking and network security.