Collaborative Research: SaTC: EDU: Adversarial Malware Analysis - An Artificial Intelligence Driven Hands-On Curriculum for Next Generation Cyber Security Workforce

协作研究：SaTC：EDU：对抗性恶意软件分析 - 下一代网络安全劳动力的人工智能驱动实践课程

• 批准号: 2230609
• 负责人: Maanak Gupta
• 金额: $ 30万
• 依托单位: Tennessee Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230609&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; EDU; Adversarial

Artificial Intelligence (AI) and Machine Learning (ML) techniques can bolster cybersecurity by aiding security administrators in detecting suspicious behaviors and initiating responses to threats. However, AL/ML technology remains susceptible to malicious exploitation, potentially leading to unintended outcomes. Therefore, it is important to ensure that AI-based decision processes are reliable in critical operational systems when facing adversarial situations. As deep learning (DL) and other AI/ML algorithms become integrated into operational systems, it is essential to defend security, privacy, and fairness of AI/ML against adversaries. This can be achieved by implementing more robust ML methods such as AI reconnaissance prevention, analysis of adversarial models, model poisoning prevention, and secure training procedures. By equipping students with the knowledge needed to secure AI in malware analysis applications, this project will foster growth of next-generation cybersecurity talent. This project will research and develop self-contained course modules focused on Adversarial Machine Learning (AML) within the context of malware analysis applications, which will transit cutting-edge research topics into the teaching and learning process. The goal of these modules is to develop students at Tennessee Tech University (TTU) and North Carolina Agricultural and Technical State University (NCAT) with specialized knowledge in this area. Course modules will include adversarial malware generation, robustness of file structure against random perturbation, poisoning attack and defense, white-box evasion attack, and surrogate model construction. The AML cyber modules will be integrated into different non-security courses such as AI/ML or data science or provided as an independent cybersecurity course. Students will acquire practical and conceptual knowledge by engaging with different AI/ML techniques for security solutions pertinent to the malware analysis domain. Additionally, students will develop advanced skills necessary for safeguarding AI systems. The interdisciplinary team, composed of experts in cybersecurity, artificial intelligence, and education, will utilize a guiding conceptual framework to strategically develop cybersecurity education modules. They will investigate the impact of these modules on learning outcomes, while refining pedagogical strategies to promote diversity and inclusion in cybersecurity education. Developed modules, instructional materials, and tutorial activities will be widely available for dissemination. This project will support integration of security and education research topics to create new knowledge in cybersecurity.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.

人工智能（AI）和机器学习（ML）技术可以通过帮助安全管理人员检测可疑行为并启动对威胁的响应来增强网络安全。但是，AL/ML技术仍然容易受到恶意剥削的影响，这可能会导致意想不到的结果。因此，重要的是要确保在面对对抗情况时，基于AI的决策过程在关键的操作系统中是可靠的。随着深度学习（DL）和其他AI/ML算法融入运营系统，因此捍卫AI/ML的安全性，隐私和公平性至关重要。这可以通过实施更强大的ML方法（例如AI侦察预防，对抗模型分析，预防模型中毒和安全训练程序）来实现。通过为学生提供在恶意软件分析应用程序中获得AI所需的知识，该项目将促进下一代网络安全人才的增长。该项目将在恶意软件分析应用程序的背景下研究和开发针对对抗机器学习（AML）的独立课程模块，该应用程序将把尖端的研究主题传输到教学过程中。这些模块的目的是在田纳西理工大学（TTU）和北卡罗来纳州农业技术州立大学（NCAT）开发学生，并在该领域具有专门知识。课程模块将包括对抗性恶意软件的产生，对随机扰动的文件结构的鲁棒性，中毒攻击和防御，白盒逃避攻击和替代模型构建。 AML网络模块将集成到不同的非安全课程中，例如AI/ML或数据科学，或作为独立网络安全课程提供的。学生将通过使用不同的AI/ML技术来获得与恶意软件分析域相关的安全解决方案，从而获得实用和概念知识。此外，学生将开发维护AI系统所需的高级技能。由网络安全，人工智能和教育专家组成的跨学科团队将利用一个指导的概念框架来战略性地开发网络安全教育模块。他们将研究这些模块对学习成果的影响，同时提高教学策略，以促进网络安全教育中的多样性和包容性。开发的模块，教学材料和教程活动将被广泛用于传播。该项目将支持安全和教育研究主题的整合，以创建网络安全方面的新知识。该奖项反映了NSF的法定使命，并被认为是通过基金会的知识分子优点和更广泛的影响评估标准来评估值得支持的。