18 citations · 38 across the 16 of their papers we have counts for
17 papers
A Systems Theoretic Approach to Online Machine Learning
Anli du Preez, Peter A. Beling, Tyler Cody
The machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a pro…
On Extending the Automatic Test Markup Language (ATML) for Machine Learning
Tyler Cody, Bingtong Li, Peter A. Beling
This paper addresses the urgent need for messaging standards in the operational test and evaluation (T&E) of machine learning (ML) applications, particularly in edge ML application…
Discovering Command and Control Channels Using Reinforcement Learning
Cheng Wang, Akshay Kakkar, Christopher Redino +7
Command and control (C2) paths for issuing commands to malware are sometimes the only indicators of its existence within networks. Identifying potential C2 channels is often a manu…
A Systems-Theoretical Formalization of Closed Systems
Niloofar Shadab, Tyler Cody, Alejandro Salado +1
There is a lack of formalism for some key foundational concepts in systems engineering. One of the most recently acknowledged deficits is the inadequacy of systems engineering prac…
Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error
Tyler Cody, Laura Freeman
Machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Out…
Active Learning with Combinatorial Coverage
Sai Prathyush Katragadda, Tyler Cody, Peter Beling +1
Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data l…
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
Lanxiao Huang, Tyler Cody, Christopher Redino +8
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detect…
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems
Tyler Cody
Transfer learning, multi-task learning, and meta-learning are well-studied topics concerned with the generalization of knowledge across learning tasks and are closely related to ge…
Core and Periphery as Closed-System Precepts for Engineering General Intelligence
Tyler Cody, Niloofar Shadab, Alejandro Salado +1
Engineering methods are centered around traditional notions of decomposition and recomposition that rely on partitioning the inputs and outputs of components to allow for component…
A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs
Tyler Cody
This paper considers key challenges to using reinforcement learning (RL) with attack graphs to automate penetration testing in real-world applications from a systems perspective. R…
Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing
Tyler Cody, Erin Lanus, Daniel D. Doyle +1
This paper demonstrates the systematic use of combinatorial coverage for selecting and characterizing test and training sets for machine learning models. The presented work adapts…
Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs
Tyler Cody, Abdul Rahman, Christopher Redino +7
Reinforcement learning (RL), in conjunction with attack graphs and cyber terrain, are used to develop reward and state associated with determination of optimal paths for exfiltrati…
Mesarovician Abstract Learning Systems
Tyler Cody
The solution methods used to realize artificial general intelligence (AGI) may not contain the formalism needed to adequately model and characterize AGI. In particular, current app…
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs
Rohit Gangupantulu, Tyler Cody, Abdul Rahman +3
Cyber attacks pose existential threats to nations and enterprises. Current practice favors piece-wise analysis using threat-models in the stead of rigorous cyber terrain analysis a…
Using Cyber Terrain in Reinforcement Learning for Penetration Testing
Rohit Gangupantulu, Tyler Cody, Paul Park +4
Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nua…
A Systems Theory of Transfer Learning
Tyler Cody, Peter A. Beling
Existing frameworks for transfer learning are incomplete from a systems theoretic perspective. They place emphasis on notions of domain and task, and neglect notions of structure a…
Empirically Measuring Transfer Distance for System Design and Operation
Tyler Cody, Stephen Adams, Peter A. Beling
Classical machine learning approaches are sensitive to non-stationarity. Transfer learning can address non-stationarity by sharing knowledge from one system to another, however, in…