Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge

Understand

Malware continues to be a major cyber threat, despite the tremendous effort that has been made to combat them.

• The number of malware in the wild steadily increases over time, meaning that we must resort to automated defense techniques.
• This naturally calls for machine learning based malware detection.
• However, machine learning is known to be vulnerable to adversarial evasion attacks that manipulate a small number of features to make classifiers wrongly recognize a malware sample as a benign one.