论文标题
AI辅助恶意软件分析：下一代网络安全劳动力的课程
AI assisted Malware Analysis: A Course for Next Generation Cybersecurity Workforce
论文作者
论文摘要
使用人工智能（AI）和机器学习（ML）来解决网络安全问题一直在行业和学术界的吸引力，部分是作为对关键系统的广泛恶意软件攻击的回应，例如云基础架构，政府办公室或医院，以及它们生成的大量数据。 AI-和ML辅助网络安全提供了数据驱动的自动化，可以使安全系统能够实时识别和应对网络威胁。但是，目前，在AI和ML培训的网络安全方面，专业人员的专业人员短缺。在这里，我们通过开发实验室密集型模块来解决这一短缺，使本科生和研究生能够在应用AI和ML技术应用于现实世界数据集中以了解网络威胁智能（CTI），恶意软件分析以及分类等其他重要主题，以及网络美食中的其他重要主题。 在这里，我们在“ AI辅助恶意软件分析”中描述了六个独立和自适应模块。主题包括：（1）CTI和恶意软件攻击阶段，（2）恶意软件知识表示和CTI共享，（3）恶意软件数据收集和功能识别，（4）AI辅助恶意软件检测，（5）恶意软件分类和归因和（6）先进的恶意软件研究主题和高级恶意研究主题和诸如对抗性学习和先进的Persistent and Advance persistent Wistection（a a）。
The use of Artificial Intelligence (AI) and Machine Learning (ML) to solve cybersecurity problems has been gaining traction within industry and academia, in part as a response to widespread malware attacks on critical systems, such as cloud infrastructures, government offices or hospitals, and the vast amounts of data they generate. AI- and ML-assisted cybersecurity offers data-driven automation that could enable security systems to identify and respond to cyber threats in real time. However, there is currently a shortfall of professionals trained in AI and ML for cybersecurity. Here we address the shortfall by developing lab-intensive modules that enable undergraduate and graduate students to gain fundamental and advanced knowledge in applying AI and ML techniques to real-world datasets to learn about Cyber Threat Intelligence (CTI), malware analysis, and classification, among other important topics in cybersecurity. Here we describe six self-contained and adaptive modules in "AI-assisted Malware Analysis." Topics include: (1) CTI and malware attack stages, (2) malware knowledge representation and CTI sharing, (3) malware data collection and feature identification, (4) AI-assisted malware detection, (5) malware classification and attribution, and (6) advanced malware research topics and case studies such as adversarial learning and Advanced Persistent Threat (APT) detection.
