Using genetic algorithm to minimize false alarms in insider threats detection of information misuse in windows environment

Abstract

Insider threats detection problem has always been one of the most difficult challenges for organizations and research community. Effective behavioral categorization of users plays a vital role for the success of any detection mechanisms. It also helps to reduce false alarms in case of insider threats. In order to achieve this, a fuzzy classifier has been implemented along with genetic algorithm (GA) to enhance the efficiency of a fuzzy classifier. It also enhances the functionality of all other modules to achieve better results in terms of false alarms. A scenario driven approach along with mathematical evaluation verifies the effectiveness of the modified framework. It has been tested for the enterprises having critical nature of business. Other organizations can adopt it in accordance with their specific nature of business, need, and operational processes. The results prove that accurate classification and detection of users were achieved by adopting the modified framework which in turn minimizes false alarms.