Simulated User Bots for Insider Threat Detection Testing
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Solution Overview
Problem
Existing security measures, such as firewalls and antivirus software, are inadequate in detecting and preventing insider threats and malware, which can cause significant damage by going undetected for long periods, and there is a need for effective testing of insider threat detection systems.
Innovation Solution
The method involves generating and injecting simulated user bots into a computing environment to emulate malicious user behavior, allowing the insider threat detection system to identify and respond to potential threats, and assessing its efficacy by determining if the system correctly identifies the simulated bot as malicious.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simulated user bots are injected into the computing environment to test insider threat detection systems, then the ability to evaluate detection accuracy and security measures is improved, but the complexity of the testing system and computational resources required increase
Solution Approach 1:
The patent creates simulated user bots that copy and emulate the behavior patterns, actions, and characteristics of real users within the computing environment. These virtual copies perform tasks identical to legitimate users, allowing the detection system to be tested with realistic behavior patterns without requiring actual user participation or complex physical testing setups.
Solution Approach 2:
The simulated user bots serve as an intermediary between the testing system and the insider threat detection system. Rather than directly testing the detection system with abstract scenarios or manual evaluation, the intermediary bots translate testing objectives into realistic user behaviors that the detection system can naturally monitor and analyze, simplifying the overall testing architecture.
2Reliability
If simulated user bots are used to emulate malicious user behavior, then the capability to assess security measures and detect insider threats is improved, but the time and computational resources required for testing increase
Solution Approach 1:
The system pre-generates simulated user bots with predefined malicious behavior patterns and characteristics before actual testing begins. These bots are prepared in advance with various threat scenarios, allowing the detection system to be tested immediately without requiring time-consuming setup or configuration during the actual assessment process.
Solution Approach 2:
The simulated user bots can be deployed periodically or in batches to test different aspects of the detection system at scheduled intervals. This allows comprehensive security assessment to be conducted through multiple targeted testing waves rather than requiring continuous or exhaustive testing, optimizing the use of computational resources and time.
Data Source
AI summary
Methods, systems, and media for testing insider threat detection systems are provided. In some embodiments, the method comprises: receiving, using a hardware processor, a first plurality of actions in a computing environment that are associated with one of a plurality of user accounts; generating a plurality of models of user behavior based at least in part on the first plurality of actions, wherein each of the plurality of models of user behavior is associated with each of the plurality of user accounts; selecting a model of user behavior from the plurality of models of user behavior, wherein the model of user behavior is associated with a malicious user type; generating a simulated user bot based on the selected model of user behavior; executing the simulated user bot in the computing environment, wherein the simulated user bot injects a second plurality of actions in the computing environment; determining whether an insider threat detection system executing within the computing environment identifies the simulated user bot as a malicious user; and transmitting a notification indicating an efficacy of the insider threat detection system based on the determination.