  Most recent studies have shown several vulnerabilities to attacks with the
potential to jeopardize the integrity of the model, opening in a few recent
years a new window of opportunity in terms of cyber-security. The main interest
of this paper is directed towards data poisoning attacks involving
label-flipping, this kind of attacks occur during the training phase, being the
aim of the attacker to compromise the integrity of the targeted machine
learning model by drastically reducing the overall accuracy of the model and/or
achieving the missclassification of determined samples. This paper is conducted
with intention of proposing two new kinds of data poisoning attacks based on
label-flipping, the targeted of the attack is represented by a variety of
machine learning classifiers dedicated for malware detection using mobile
exfiltration data. With that, the proposed attacks are proven to be
model-agnostic, having successfully corrupted a wide variety of machine
learning models; Logistic Regression, Decision Tree, Random Forest and KNN are
some examples. The first attack is performs label-flipping actions randomly
while the second attacks performs label flipping only one of the 2 classes in
particular. The effects of each attack are analyzed in further detail with
special emphasis on the accuracy drop and the misclassification rate. Finally,
this paper pursuits further research direction by suggesting the development of
a defense technique that could promise a feasible detection and/or mitigation
mechanisms; such technique should be capable of conferring a certain level of
robustness to a target model against potential attackers.
