Abstract
Nowadays it is very widespread to see attacks in the system. The attackers try automated tools and programs to attempt and gain access to the data of the users. However, for attackers, it is hard to boycott system calls. System calls are used by the user-level processes to request the different services from the kernel of the operating system. It is very difficult for the attacks to evade the system calls. The system calls are used to make every basic interaction between the operating system and program. The system performs allocating and deallocating memory, closing, reading, renaming and the opening of files, and starting and stopping a process. The size of the system log can be overwhelmingly huge, which makes it hard for the system admins to extract the useful information from it. In this project, we propose to analyze and visualize the system calls so that it can help the system administrators to extract information from the log easily and identify suspicious activities and behavior. The steps in the project include data collection/gathering, data exploration, data cleaning, data transformation, data mining, and data visualization. This approach helps to extract important information from the system calls by using data mining and machine learning algorithms. The statistics obtained through system call analysis and visualization provide valuable information about the system activities and reveal important patterns. This information and patterns can help identify suspicious behavior which might be related to attacks.