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The Maine, the Media, and the American Mind: Exploring the Outbreak of the Spanish American War
Full text linkDeep VULMAN: A Deep Reinforcement Learning-enabled Cyber Vulnerability Management Framework
Full text linkCyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as the number of deficiencies in these systems is increasing at a significantly higher rate compared to the expansion rate of the security teams to mitigate them. The current approaches in cyber vulnerability management are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecting vulnerabilities for mitigation. These approaches are also constrained by the sub-optimal distribution of resources, providing no flexibility to adjust their response to fluctuations in vulnerability arrivals. We propose a novel framework, Deep VULMAN, consisting of a deep reinforcement learning agent and an integer programming method to fill this gap in cyber vulnerability management process. Our sequential decision-making framework, first, determines the near-optimal amount of resources to be allocated for mitigation under uncertainty for a given system state, and then determines the optimal set of prioritized vulnerability instances for mitigation. Results show that our framework outperforms the current methods in prioritizing the selection of important organization-specific vulnerabilities, on both simulated and real-world vulnerability data, observed over a one-year period
The Power of Inspiration: How Joan of Arc Turned the Tide of the Hundred Years\u27 War
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Discrete Zombie Apocalypse: A Mathematical Modeling Course Project
No full textFor undergraduate mathematical modeling courses, a successful semester project can reinforce key learning objectives while enabling creativity and developing critical thinking skills. However, course directors often struggle in developing novel project ideas and balancing the tradeoff between grading burden and project complexity. At the U.S Military Academy, we take an open-ended and discovery-learning approach to the freshman level math modeling project. This article outlines one successful project involving a Zombie Apocalypse scenario along with student responses. To assist the students, we promote flexibility, scaffold the modeling process with in-progress reviews, and train students on how to write concise executive summaries
Building an American: The United States Army and the Carlisle Indian Industrial School
Full text linkUnintended Consequences: The Long-Term Effects of the United States Security Relationship with the United Kingdom
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Though War Break Out Against Me: The Course and Effects of Religious Revival in the Confederate Armies
Full text linkGraph Representation Learning for Context-Aware Network Intrusion Detection
Full text linkDetecting malicious activity using a network intrusion detection system (NIDS) is an ongoing battle for the cyber defender. Increasingly, cyber-attacks are sophisticated and occur rapidly, necessitating the use of machine/deep learning (ML/DL) techniques for network intrusion detection. Traditional ML/DL techniques for NIDS classifiers, however, are often unable to sufficiently find context-driven similarities between the various network flows and/or packet captures. In this work, we leverage graph representation learning (GRL) techniques to successfully detect adversarial intrusions by exploiting the graph structure of NIDS data to derive context awareness, as graphs are a universal language for describing entities and their relationships. We explore several methods for NIDS data graph representation at both the network flow and packet level utilizing the CIC-IDS2017 dataset. We leverage graph neural networks and graph embedding algorithms to create a context-aware network intrusion detection system. Results indicate that adding context derived from GRL improves performance for detecting attacks. Our highest-scoring classifier incorporated both GNN embeddings and flow-level features and achieved an accuracy of 99.9%. Adding GRL methods to augment the flow/packet features improved accuracy by as much as 52.41%