USMA Digital Commons

USMA Digital Commons is a secure digital service managed by the United States Military Academy Library to make the work of USMA scholars freely available, while also ensuring these resources are organized to preserve the legacy of USMA scholarship. The mission of USMA Digital Commons is to showcase the academic impact and intellectual capital that has become synonymous with the celebrated heritage of educational prowess attributed to the Long Gray Line. Scholarship submitted to USMA Digital Commons benefits from added visibility and discoverability via Google Scholar in addition to the use of persistent URLs that will provide enduring access to the work over time.

 

Communities

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Recent Submissions

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SeNet-I: An Approach for Detecting Network Intrusions Through Serialized Network Traffic Images
(Engineering Applications of Artificial Intelligence, 2023-09-27) Bastian, Nathaniel D.; Farrukh, Yasir; Wali, Syed; Khan, Irfan
The exponential growth of the internet and inter-connectivity has resulted in an extensive increase in network size and the corresponding data, which has led to numerous novel attacks that pose significant challenges to network security. However, conventional network security approaches predominantly rely on the metadata of network traffic, utilized in numeric form, which is becoming ineffective against new attacks that hide within the content of the traffic. Therefore, it raises the need for security systems to adapt to the changing dynamics of network attacks. To address this issue, we propose a new approach called SeNet-I that leverages computer vision capabilities to combine low-level features and develop a more abstract and high-level representation of network traffic without requiring feature engineering. The proposed approach utilizes the raw network traffic information and transforms it into serialized three-channel images, which are employed as input to a proposed deep concatenated convolutional neural network model. Additionally, SeNet-I can easily incorporate packet level information, which is often challenging for conventional approaches due to its high dimensionality. To demonstrate the effectiveness of the proposed approach, we tested SeNet-I on both packet-based and flow-based network traffic, comparing it with current state-of-the-art methods and different image-based approaches. With F1 scores of 96% and 83% achieved in the multi-class classification of flow-based and packet-based network intrusion detection, our proposed approach outperformed other existing methods in the literature. Lastly, we discussed the advantages and limitations of the proposed method.
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Space Operations Risk Tool
(United States Service Academies, 2023) Musante, Robert IV; Vuille-Kowing, James; Connor, Patrick; Tejada-Soliz, Angela; Kudlak, Zachary; Peterson, Eric
No abstract provided.
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USCG Retention Study
(United States Service Academies, 2023) Rickey, Montgomery; Williams, Matthew
No abstract provided.
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Numerical Analysis of a Combustion Model for Layered Media Via Mathematical Homogenization
(United States Service Academies, 2023) Riggs, Jessica M.; Soane, Ana Maria
We propose to investigate a mathematical model for combustion in a rod made of periodically alternating thin layers of two combustible materials such as those occurring in gun propellants. We apply the homogenization theory to resolve the fast oscillations of the model’s coefficients across adjacent layers, and set up numerical simulations to better understand the reactions occurring in such media.
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Modeling Vegetation-Erosion Dynamics using Differential Equations with Human Factors
(United States Service Academies, 2023) Cowden, Evan M.; Hallare, Malia
No abstract provided.