As the popularity of the Android platform grows, the number of malicious apps targeting this platform grows along with it. Accordingly, as the number of malicious apps increases, so too does the need for an automated system which can effectively detect and classify these apps and their families. This paper presents a new system for classifying malware by leveraging the text strings present in an app’s binary files. This approach was tested using over 5,000 apps from 14 different malware families and was able to classify samples with over 99% accuracy while maintaining a false positive rate of 2.0%.