Identification of malicious behavior and creation of an attacker's profile in learning management systems
Abstract
Identification of malicious behavior and creation of an attacker's profile in learning management systems
Incoming article date: 06.04.2024In the process of ensuring information security, an important element is the protection of data from malicious influence. One of the stages of protection is to identify the source of the threat. The source of the threat may be the attacker himself, acting through his own user profile. Detecting and identifying a malicious profile in this case is a key element of building protection. In the context of user profiling, well-researched systems are social networks. Learning Management Systems (LMS), despite their similarity to social networks, remain devoid of scientific attention. Due to the similarity with social networks and the increasing introduction of learning management systems into information structures, this type of system is becoming a vulnerable point. There are several approaches and methods for detecting malicious activity, including statistical methods, machine learning methods, and rule-based methods. Our proposed approach combines a rule-based system with machine learning algorithms to improve the accuracy of detecting malicious activity. A rule-based system provides a detailed view of potential anomalies, while a machine learning algorithm provides a more detailed view of specific attacks. This paper highlights the aspects of determining malicious activity and user profiling in learning management systems and provides an example of one of the methods.
Keywords: information security, information system, social network, learning management system, user profiling, malicious behavior, classification