  Modern computing systems have led cyber adversaries to create more
sophisticated malware than was previously available in the early days of
technology. Dated detection techniques such as Anti-Virus Software (AVS) based
on signature-based methods could no longer keep up with the demand that
computer systems required of them. The complexity of modern malware has led to
the development of contemporary detection techniques that use the machine
learning field and hardware to boost the detection rates of malicious software.
These new techniques use Hardware Performance Counters (HPCs) that form a
digital signature of sorts. After the models are fed training data, they can
reference these HPCs to classify zero-day malware samples. A problem emerges
when malware with no comparable HPC values comes into contact with these new
techniques. We provide an analysis of several machine learning and deep
learning models that run zero-day samples and evaluate the results from the
conversion of C++ algorithms to a hardware description language (HDL) used to
begin a hardware implementation. Our results present a lack of accuracy from
the models when running zero-day malware data as our highest detector, decision
tree, was only able to reach 91.2% accuracy and had an F1-Score of 91.5% in the
form of a decision tree. Next, through the Receiver Operating Curve (ROC) and
area-under-the-curve (AUC), we can also determine that the algorithms did not
present significant robustness as the largest AUC was only 0.819. In addition,
we viewed relatively high overhead for our ensemble learning algorithm while
also only having an 86.3% accuracy and 86% F1-Score. Finally, as an additional
task, we adapted the one rule algorithm to fit many rules to make malware
classification understandable to everyday users by allowing them to view the
regulations while maintaining relatively high accuracy.
