REU Site: Data-driven Security

REU 站点：数据驱动的安全

• 批准号: 1950599
• 负责人: Francesca Spezzano
• 金额: $ 36.45万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1950599&HistoricalAwards=false
• 关键词: REU; Site; Data; driven; Security

This award establishes a new Research Experiences for Undergraduates (REU) Site focused on data-driven security at Boise State University. Data-driven security is an emerging interdisciplinary field that applies data science and artificial intelligence to mitigate cyberattacks and other security risks and threats. Undergraduate students will participate in summer research activities with faculty mentors from the computer science and mathematics disciplines. The students will work in teams to explore important research questions and will also participate in other professional development activities that will prepare them for future careers in the computing fields. The site will target students from groups traditionally under-represented in computer science as well as students from two-year colleges in the Pacific Northwest.The REU Site will feature transformative interdisciplinary research in the field of data-driven security. Research problems include: a novel data-driven approach to learn the characteristic function of a coalition game that models a covert network to improve key actor identification; new approaches to detect misbehavior and mitigate misinformation; innovative robust traffic-flow behavioral model extraction for detecting anomalous situations in enterprise networks; development of new lightweight cryptographic algorithms that are robust to both algorithmic weaknesses and side-channel attacks performed with machine learning. All of the research requires deep integration of data science, artificial intelligence, mathematics, and security. Students will learn to work in teams and communicate their results to a diverse audience by participating in activities that use investigative methodologies based on constructive dialogue and collaborative design. The projects have the potential to broaden knowledge in several domains, including game theory, intrusion detection systems, misinformation mitigation, and lightweight cryptography.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.

该奖项在博伊西州立大学建立了一个新的本科生研究体验 (REU) 网站，重点关注数据驱动的安全性。 数据驱动的安全是一个新兴的跨学科领域，它应用数据科学和人工智能来减轻网络攻击和其他安全风险和威胁。 本科生将与计算机科学和数学学科的导师一起参加夏季研究活动。学生们将通过团队合作探索重要的研究问题，并将参加其他专业发展活动，为他们未来在计算机领域的职业生涯做好准备。该网站的目标受众是传统上计算机科学领域代表性不足的群体的学生以及太平洋西北地区两年制大学的学生。REU 网站将以数据驱动安全领域的变革性跨学科研究为特色。研究问题包括：一种新颖的数据驱动方法，用于学习联盟博弈的特征函数，该博弈对隐蔽网络进行建模，以提高关键参与者的识别能力；检测不当行为和减少错误信息的新方法；创新的稳健流量行为模型提取，用于检测企业网络中的异常情况；开发新的轻量级加密算法，该算法对算法弱点和机器学习执行的旁道攻击都具有鲁棒性。所有的研究都需要数据科学、人工智能、数学和安全的深度融合。学生将学习团队合作，并通过参与基于建设性对话和协作设计的调查方法的活动，向不同的受众传达他们的结果。 这些项目有潜力拓宽多个领域的知识，包括博弈论、入侵检测系统、错误信息缓解和轻量级密码学。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Identifying ATT&CK Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability (Student Abstract)

识别 ATT

• 发表时间: 2022
• 期刊: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence - Student Abstract and Poster Program
• 作者: Fairbanks, J.;Orbe, A.;Patterson, C.;Serra, E.;Scheepers, M.
• 通讯作者: Scheepers, M.

Deep Learning Based Side Channel Attacks on Lightweight Cryptography (Student Abstract)

基于深度学习的轻量级密码学侧信道攻击（学生摘要）

• 发表时间: 2022
• 期刊: Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence - Student Abstract and Poster Program
• 作者: Benjamin, A.;Herzoff, J.;Babinkostova, L.;and Serra, E.
• 通讯作者: and Serra, E.

GAPS: Generality and Precision with Shapley Attribution

GAPS：Shapley 归因的通用性和精确性

• 发表时间: 2022
• 期刊: 2022 {IEEE} International Conference on Big Data (Big Data
• 作者: Daley, Brian;Ratul, Qudrat E;Serra, E.;Cuzzocrea, Alfredo
• 通讯作者: Cuzzocrea, Alfredo

Modeling Misinformation Diffusion in Social Media: Beyond Network Properties

对社交媒体中的错误信息扩散进行建模：超越网络属性

• DOI: 10.1109/cogmi52975.2021.00030
• 发表时间: 2021
• 作者: Spezzano, Francesca

Predicting RNA Mutation Effects through Machine Learning of High-Throughput Ribozyme Experiments (Student Abstract)

• DOI: 10.1609/aaai.v36i11.21629
• 发表时间: 2022-06
• 作者: Joey Kitzhaber;Ashlyn Trapp;James D. Beck;Edoardo Serra;Francesca Spezzano;Eric J. Hayden;J. Roberts