  Cyber-crimes have become a multi-billion-dollar industry in the recent years.
Most cybercrimes/attacks involve deploying some type of malware. Malware that
viciously targets every industry, every sector, every enterprise and even
individuals has shown its capabilities to take entire business organizations
offline and cause significant financial damage in billions of dollars annually.
Malware authors are constantly evolving in their attack strategies and
sophistication and are developing malware that is difficult to detect and can
lay dormant in the background for quite some time in order to evade security
controls. Given the above argument, Traditional approaches to malware detection
are no longer effective. As a result, deep learning models have become an
emerging trend to detect and classify malware. This paper proposes a new
convolutional deep learning neural network to accurately and effectively detect
malware with high precision. This paper is different than most other papers in
the literature in that it uses an expert data science approach by developing a
convolutional neural network from scratch to establish a baseline of the
performance model first, explores and implements an improvement model from the
baseline model, and finally it evaluates the performance of the final model.
The baseline model initially achieves 98% accurate rate but after increasing
the depth of the CNN model, its accuracy reaches 99.183 which outperforms most
of the CNN models in the literature. Finally, to further solidify the
effectiveness of this CNN model, we use the improved model to make predictions
on new malware samples within our dataset.
