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

合作研究：CISE-MSI：RCBP-RF：SaTC：网络安全中基于人工智能的异常检测的研究能力建设

• 批准号: 2131144
• 负责人: Dongwon Lee
• 金额: $ 10万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131144&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; RCBP

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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。该协作项目是Tuskegee University（TU），HBCU机构和宾夕法尼亚州立大学（PSU）（PSU）的R1研究密集型机构，将促进Cyber​​section的良好探讨，以促进研究和教育的良好发展，从而促进Cyber​​security的良好态度，以促进研究和教育的发展，从而促进Cyber​​security的进展，以实现良好的研究，并将其共同促进研究，以促进研究和教育，从而促进Cyber​​security的进展，以实现良好的范围。入侵攻击 - 即在网络上未经授权的活动，涉及窃取宝贵的资源和/或危害网络的安全性。特别是，该团队的解决方案将基于基于AI的异常检测框架，将入侵攻击视为罕见或异常观察，这些观察结果偏离其他观察结果，利用机器学习，自然语言处理和数据科学技术的最新进步来检测这些出发。基于研究结果和协作工作，项目团队将提高TU在网络安全性，机器学习和数据科学方面的研究能力，并增强课程，以向TU和PSU的本科生和研究生讲授这些主题和最新发现。检测。例如，该团队将使用高级技术（例如，通过通用对抗性网络，共同注意力网络，很少的射击学习和对抗性示例）探索最新的成功失误，并通过PSU团队扩展/扩展/将其扩展到其他入侵检测任务。要开发的改进的广告将包括（1）用于收集，标签，增强和增强高级分析数据的新策略，（2）用于数据表示，功能/表示学习和系统行为分类的解决方案，以及（3）用于开发广告工具的实施框架。该团队期望新技术有助于以低阳性速率在网络入侵检测中实现最新的准确性。此外，该项目将为从历史上占代表性不足的群体中的人们提供研究机会，这将使学生能够在网络安全和机器学习方面购买研究生研究。该项目由计算机，信息科学和工程少数派服务机构研究机构共同资助，并促进了有能力的研究（Epscore nation ant Stat of Stat of Stat of Stat of Stat of Enders to ant thers evers evers to ant ters evers to ant ters evers to ant ters evers evers evers evers to ant ters evers evers evers evers tos evers evers evers。使用基金会的智力优点和更广泛的影响审查标准通过评估来支持。

项目成果

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

• DOI: 10.1145/3606274.3606276
• 发表时间: 2022-10
• 期刊: ACM SIGKDD Explorations Newsletter
• 作者: Adaku Uchendu;Thai Le;Dongwon Lee

UPTON: Preventing Authorship Leakage from Public Text Release via Data Poisoning

• DOI: 10.18653/v1/2023.findings-emnlp.800
• 发表时间: 2022-11
• 作者: Ziyao Wang;Thai Le;Dongwon Lee

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

以毒攻毒：法学硕士在制作和检测难以捉摸的虚假信息方面的双重作用

• DOI: 10.18653/v1/2023.emnlp-main.883
• 发表时间: 2023
• 期刊: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
• 作者: Lucas, Jason;Uchendu, Adaku;Yamashita, Michiharu;Lee, Jooyoung;Rohatgi, Shaurya;Lee, Dongwon
• 通讯作者: Lee, Dongwon

MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark

MULTITuDE：大规模多语言机器生成的文本检测基准

• DOI: 10.18653/v1/2023.emnlp-main.616
• 发表时间: 2023
• 期刊: Association for Computational Linguistics
• 作者: Macko, Dominik;Moro, Robert;Uchendu, Adaku;Lucas, Jason;Yamashita, Michiharu;Pikuliak, Matúš;Srba, Ivan;Le, Thai;Lee, Dongwon;Simko, Jakub
• 通讯作者: Simko, Jakub

Information Operations in Turkey: Manufacturing Resilience with Free Twitter Accounts

土耳其的信息运营：通过免费 Twitter 帐户实现制造弹性

• 发表时间: 2023
• 期刊: 17th Int'l AAAI Conf. on Web and Social Media (ICWSM
• 作者: Merhi, Maya Merhi;Rajtmajer, Sarah;Lee, Dongwon
• 通讯作者: Lee, Dongwon