DroidMLN: A Markov Logic Network Approach to Detect Android Malware

Abstract

Traditional data mining mechanisms with their robustly defined classification techniques have certain limitations to express to what extent the class labels of the test data hold. This problem leads to the fact that a false positive or false negative data point has no quantitative value to express to what degree it is false/true. This situation becomes much severe when it comes to the problem of Malware detection for a growing business market like Android applications. To address the need for a more fine grained model to measure the fitness of the classification we used Markov Logic Network for the first time to detect Android Malwares.