We investigate artificial intelligence and machine learning methods for optimizing the adversarial behavior of agents in cybersecurity simulations. Our cybersecurity simulations integrate the modeling of agents launching Advanced Persistent Threats (APTs) with the modeling of agents using detection and mitigation mechanisms against APTs. This simulates the phenomenon of how attacks and defenses coevolve. The simulations and machine learning are used to search for optimal agent behaviors. The central question is: under what circumstances, is one training method more advantageous than another? We adapt and compare a variety of deep reinforcement learning (DRL), evolutionary strategies (ES) and Monte Carlo Tree Search methods within Connect 4, a baseline game environment, and on both a simulation supporting a simple APT threat model, SNAPT, as well as CyberBattleSim, an open-source cybersecurity simulation. Our results show that when attackers are trained by DRL and ES algorithms, as well as when they are trained with both algorithms being used in alternation, they are able to effectively choose complex exploits that thwart a defense. The algorithm that combines DRL and ES achieves the best comparative performance when attackers and defenders are simultaneously trained, rather than when each is trained against its non-learning counterpart.
Student reflection is thought to be an important part of retaining and understanding knowledge gained in a course. Using natural language processing, we analyze and interpret student reflections from Massive Open Online Courses (MOOCs) to understand the students' sentiments and problem-solving procedures. The reflections are free text responses to questions from MIT 6.00.1x, an introductory programming MOOC. We compare different sentiment analysis methods, and conclude that the best-performing methods can robustly classify sentiment of student responses. In addition, we develop methods to analyze student problem-solving procedures using sentence parsing and topic modeling. We find our method can distinguish some common problem-solving procedures such as utilizing course resources.
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