  Machine learning based malware detection techniques rely on grayscale images
of malware and tends to classify malware based on the distribution of textures
in graycale images. Albeit the advancement and promising results shown by
machine learning techniques, attackers can exploit the vulnerabilities by
generating adversarial samples. Adversarial samples are generated by
intelligently crafting and adding perturbations to the input samples. There
exists majority of the software based adversarial attacks and defenses. To
defend against the adversaries, the existing malware detection based on machine
learning and grayscale images needs a preprocessing for the adversarial data.
This can cause an additional overhead and can prolong the real-time malware
detection. So, as an alternative to this, we explore RRAM (Resistive Random
Access Memory) based defense against adversaries. Therefore, the aim of this
thesis is to address the above mentioned critical system security issues. The
above mentioned challenges are addressed by demonstrating proposed techniques
to design a secure and robust cognitive system. First, a novel technique to
detect stealthy malware is proposed. The technique uses malware binary images
and then extract different features from the same and then employ different
ML-classifiers on the dataset thus obtained. Results demonstrate that this
technique is successful in differentiating classes of malware based on the
features extracted. Secondly, I demonstrate the effects of adversarial attacks
on a reconfigurable RRAM-neuromorphic architecture with different learning
algorithms and device characteristics. I also propose an integrated solution
for mitigating the effects of the adversarial attack using the reconfigurable
RRAM architecture.
