Collaborative Research: CISE-MSI: RCBP-RF: SaTC: Building Research Capacity in AI Based Anomaly Detection in Cybersecurity

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This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This collaborative project between Tuskegee University (TU), a HBCU institution, and the Pennsylvania State University (PSU), an R1 research-intensive institution, is to jointly promote research and education excellence in cybersecurity through the research and development of advanced network intrusion detection solutions to accurately and quickly detect intrusion attacks -- i.e., unauthorized activities on a network that involve stealing valuable resources and/or jeopardize the security of the network. In particular, the team’s solutions will be based on an AI based anomaly detection framework, treating intrusion attacks as rare or anomalous observations that deviate from other observations, exploiting recent advancements in machine learning, natural language processing, and data science techniques to detect these deviations. Based on the research results and collaboration efforts, the project team will improve TU’s research capacity in cybersecurity, machine learning, and data science, and enhance the curriculum for teaching these topics and latest findings to undergraduate and graduate students at both TU and PSU.In this project, the team will explore how to advance existing anomaly detection systems (ADS) through investigating ways to exploit and advance state-of-the-art methods in data science and machine learning in the context of network intrusion detection. For instance, the team will explore the recent successes in detecting subtle misinformation using advanced techniques (e.g., data augmentation via generative adversarial networks, co-attention networks, few-shot learning, and adversarial examples) by the PSU team and extend/apply them to other intrusion detection tasks. The improved ADS to be developed will include (1) novel strategies for collecting, labeling, enhancing, and augmenting data for advanced analytics, (2) solutions for data representation, feature/representation learning, and classification of system behaviors, and (3) an implementation framework for developing ADS tools. The team expects the new techniques to help achieve state-of-the-art accuracy in network intrusion detection with low false-positive rates. Further, the project will provide research opportunities for people from historically underrepresented groups in computing that will enable students to pursue graduate studies in cybersecurity and machine learning.This project is jointly funded by the Computer and Information Science and Engineering Minority-Serving Institutions Research Expansion Program (CISE-MSI) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.