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Found 19 papers, 0 papers with code
On Extending the Automatic Test Markup Language (ATML) for Machine Learning
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 applications embedded in systems like robots, satellites, and unmanned vehicles.
A Systems Theoretic Approach to Online Machine Learning
The framework is formulated in terms of input-output systems and is further divided into system structure and system behavior.
Discovering Command and Control (C2) Channels on Tor and Public Networks Using Reinforcement Learning
Results on a typical network configuration show that the RL agent can automatically discover resilient C2 attack paths utilizing both Tor-based and conventional communication channels, while also bypassing network firewalls.
Discovering Command and Control Channels Using Reinforcement Learning
Command and control (C2) paths for issuing commands to malware are sometimes the only indicators of its existence within networks.
Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space
Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains.
A Systems-Theoretical Formalization of Closed Systems
There is a lack of formalism for some key foundational concepts in systems engineering.
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle.
Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error
Results show that coverage metrics can correlate with classification error.
Active Learning with Combinatorial Coverage
The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain
Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR).
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems
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 general intelligence.
Core and Periphery as Closed-System Precepts for Engineering General Intelligence
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-level properties to hold after their composition.
Discovering Exfiltration Paths Using Reinforcement Learning with Attack Graphs
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 exfiltration of data in enterprise networks.
Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing
In contrast to prior work which has focused on the use of coverage in regard to the internal of neural networks, this paper considers coverage over simple features derived from inputs and outputs.
Mesarovician Abstract Learning Systems
Herein, Mesarovician abstract systems theory is used as a super-structure for learning.
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs
Cyber attacks pose existential threats to nations and enterprises.
Using Cyber Terrain in Reinforcement Learning for Penetration Testing
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 nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain.
A Systems Theory of Transfer Learning
We interpret existing frameworks in terms of ours and go beyond existing frameworks to define notions of transferability, transfer roughness, and transfer distance.
Empirically Measuring Transfer Distance for System Design and Operation
We consider the use of transfer distance in the design of machine rebuild procedures to allow for transferable prognostic models.
