標題: Feature-Selection-Based Ransomware Detection with Machine Learning of Data Analysis
作者: Wan, Yu-Lun
Chang, Jen-Chun
Chen, Rong-Jaye
Wang, Shiuh-Jeng
資訊工程學系
Department of Computer Science
關鍵字: component;ransomware;feature selection;intrusion detection system;data analysis
公開日期: 1-Jan-2018
摘要: Ransomwares are continuously produced in underground markets such that increasingly high-level and sophisticated ransomwares are spreading all over the world, significantly affecting individuals, businesses, governments, and countries. To prevent large-scale attacks, most companies buy intrusion detection systems to alert regarding any abnormal network behavior. However, they cannot be detected using conventional signature-based detection even though ransomwares belong to the same family. In this study, a method is provided to develop a network intrusion detection model that is based on big data technology. The system uses Argus for packet preprocessing, merging, and labeling the known malicious data. A concept of Biflow was proposed to replace the packet data. Further, we observe that the data size is reduced to 1000:1. Additionally, the characteristics of a complete traffic are obtained. Six feature selection algorithms were combined to achieve a better accuracy in terms of classification. Finally, the decision tree model of the supervised machine learning was used to enhance the performance of intrusion detection system.
URI: http://hdl.handle.net/11536/150773
期刊: PROCEEDINGS OF 2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS)
起始頁: 85
結束頁: 88
Appears in Collections:Conferences Paper