Enterprise level centric secure system administration for analysis, detection and prevention of vulnerabilities, insider attacks in multi-tenants distribution environment.

The vulnerability analysis and scanning are playing a major key role in the evolution of network and cyber security for analyzing the weakness of data entry points while the data is transmitted from enterprise to cloud and cloud to enterprise. The vulnerability scan and analysis are very important to the enterprise for identifying their networking faintness, attacker-back doors, and router logs. The vulnerability scanning methods will be accomplished by the enterprise IT team and third-party security service provided with the implications of auditing and authentication rules. The insider threats and attacks can pretense a real defense risk to the various internal multi-tenants of various enterprises and companies. This paper has been composed of three major objectives as Analysis of insider attacking types along with their implications by the supporting attributes matrices; Description of prevention methods of insider attacks and threats; Execution of test-cases for avoiding various vulnerabilities and insider attacks. It has analyzed the risk factors which impacted based on enterprise-level roles, vulnerability type and supporting parameters for committing an inside attack. This research has described the efficient security scenario to avoid or reduce the vulnerabilities and insider attacking complexities. This research has explored the enterprise-level vulnerability scanning methods and process, which is more helpful for the cyber security learners for gaining in-depth knowledge of various device, network and operating system-based vulnerabilities and their supporting application-level affected files. The proposed methodology is evaluated using different operating systems framed with different VS models and test cases experimented, tested on various enterprise level centric servers with integration of eight multitenants, four head-tenants along with 152 end-users and achieved a 92 percent of success rate. Test cases are executed based on the type of attacks and threats with the vulnerability model for analyzing the nature of the attack, its cause, the region of the attacks and the data leakage percentage. This research is more helping the enterprise-level cyber security experts and managers to reduce the various vulnerabilities with relevant insider attacks by building an efficient monitoring intelligent system. [ABSTRACT FROM AUTHOR]