Adversarial Learning of Hybrid Representation

该项目旨在设计和实现一个基础的深度表示学习框架，用于早期检测，分类和防御新兴恶意软件，通过混合表示学习，行为图挖掘和符号对抗学习，通过结构化和非结构化异构信息捕获其底层行为，以发现和防御未知的恶意软件家族，从而显著提高了现有分类器和检测器的准确性和鲁棒性。由此产生的表示学习框架将增强国家安全，以保护用户隐私，减少欺诈交易造成的数百万美元损失，并抵御网络攻击。

This project aims to design and implement a foundational deep representation learning framework for early detection, classification and defense of emerging malware by capturing their underlying behaviours via structured and unstructured heterogeneous information through hybrid representation learning, behaviour graph mining, and symbolic adversarial learning to discover and defend unknown malware families, thereby significantly boosting the accuracy and robustness of existing classifiers and detectors. The resulting representation learning framework will enhance the national security to protect user privacy, reducing the multi-million-dollar loss caused by fraudulent transactions, and defending against cyber attacks.