Machine learning is at risk of being attacked.
As companies continue to depend on
machine learning to solve their problems,
more sophisticated attacks are being created
to undermine and take advantage of machine
learning algorithms. Worse, these machine
learning attacks can have adverse effects on
our physical world, like forcing a self-driving
car to run a stop sign. MLsploit is a framework
designed to solve this problem by allowing
operators to evaluate their trained machine
learning models against a variety of attacks in
order to strengthen them. MLsploit focuses not
just on image, video, and audio data, but also
contains information security datasets used to
detect malware and defend against network
intrusions. Using MLsploit, a company can
evaluate machine learning models trained on
a number of different datasets which provide
valuable services to themselves and to their
customers.