  In this paper, we develop four malware detection methods using Hamming
distance to find similarity between samples which are first nearest neighbors
(FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and
k-medoid based nearest neighbors (KMNN). In our proposed methods, we can
trigger the alarm if we detect an Android app is malicious. Hence, our
solutions help us to avoid the spread of detected malware on a broader scale.
We provide a detailed description of the proposed detection methods and related
algorithms. We include an extensive analysis to asses the suitability of our
proposed similarity-based detection methods. In this way, we perform our
experiments on three datasets, including benign and malware Android apps like
Drebin, Contagio, and Genome. Thus, to corroborate the actual effectiveness of
our classifier, we carry out performance comparisons with some state-of-the-art
classification and malware detection algorithms, namely Mixed and Separated
solutions, the program dissimilarity measure based on entropy (PDME) and the
FalDroid algorithms. We test our experiments in a different type of features:
API, intent, and permission features on these three datasets. The results
confirm that accuracy rates of proposed algorithms are more than 90% and in
some cases (i.e., considering API features) are more than 99%, and are
comparable with existing state-of-the-art solutions.
