Project

System call analysis and visualization

Project (M.S., Computer Science)--California State University, Sacramento, 2018.

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.

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.

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