  As the prevalence and everyday use of machine learning algorithms, along with
our reliance on these algorithms grow dramatically, so do the efforts to attack
and undermine these algorithms with malicious intent, resulting in a growing
interest in adversarial machine learning. A number of approaches have been
developed that can render a machine learning algorithm ineffective through
poisoning or other types of attacks. Most attack algorithms typically use
sophisticated optimization approaches, whose objective function is designed to
cause maximum damage with respect to accuracy and performance of the algorithm
with respect to some task. In this effort, we show that while such an objective
function is indeed brutally effective in causing maximum damage on an embedded
feature selection task, it often results in an attack mechanism that can be
easily detected with an embarrassingly simple novelty or outlier detection
algorithm. We then propose an equally simple yet elegant solution by adding a
regularization term to the attacker's objective function that penalizes
outlying attack points.
