  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. The state-of-the-art is that there
are no effective countermeasures against these attacks. Inspired by the
AICS'2019 Challenge, we systematize a number of principles for enhancing the
robustness of neural networks against adversarial malware evasion attacks. Some
of these principles have been scattered in the literature, but others are
proposed in this paper for the first time. Under the guidance of these
principles, we propose a framework and an accompanying training algorithm,
which are then applied to the AICS'2019 challenge. Our experimental results
have been submitted to the challenge organizer for evaluation.
