  Label manipulation attacks are a subclass of data poisoning attacks in
adversarial machine learning used against different applications, such as
malware detection. These types of attacks represent a serious threat to
detection systems in environments having high noise rate or uncertainty, such
as complex networks and Internet of Thing (IoT). Recent work in the literature
has suggested using the $K$-Nearest Neighboring (KNN) algorithm to defend
against such attacks. However, such an approach can suffer from low to wrong
detection accuracy. In this paper, we design an architecture to tackle the
Android malware detection problem in IoT systems. We develop an attack
mechanism based on Silhouette clustering method, modified for mobile Android
platforms. We proposed two Convolutional Neural Network (CNN)-type deep
learning algorithms against this \emph{Silhouette Clustering-based Label
Flipping Attack (SCLFA)}. We show the effectiveness of these two defense
algorithms - \emph{Label-based Semi-supervised Defense (LSD)} and
\emph{clustering-based Semi-supervised Defense (CSD)} - in correcting labels
being attacked. We evaluate the performance of the proposed algorithms by
varying the various machine learning parameters on three Android datasets:
Drebin, Contagio, and Genome and three types of features: API, intent, and
permission. Our evaluation shows that using random forest feature selection and
varying ratios of features can result in an improvement of up to 19\% accuracy
when compared with the state-of-the-art method in the literature.

Machine Learning;Artificial Intelligence;Machine Learning