We present a new method of classifying previously unseen Android applications as malware or benign. The algorithm starts with a large set of features: the frequencies of all possible n-byte sequences in the application's byte code. Principal components analysis is applied to that frequency matrix in order to reduce it to a low-dimensional representation, which is then fed into any of several classification algorithms. We utilize the implicitly restarted Lanczos bidiagonalization algorithm and exploit the sparsity of the n-gram frequency matrix in order to efficiently compute the low-dimensional representation. When trained upon that low-dimensional representation, several classification algorithms achieve higher accuracy than previous work.

Recent work on predictive state representation (PSR) models has focused on using predictions of the outcomes of open-loop action sequences as state. These predictions answer questions of the form “What is the probability of seeing observation sequence o1, o2,..., oN if the agent takes action sequence a1, a2,..., aN from some given history?” We would like to ask more expressive questions in our representation of state, such as “If I behave according to some policy until I terminate, what will be my last observation?” We extend the linear PSR framework to answer questions like these about options – temporally extended, closed-loop courses of action – bounding the size of the linear PSR needed to model questions about a certain class of options. We introduce a hierarchical PSR (HPSR) that can make predictions about both options and primitive action sequences and show empirical results from learning HPSRs in simple domains. Existing work with predictive state representations (PSRs) focuses on using predictions about open-loop action sequences as state. These predictions answer questions of the form “What is the probability of seeing observation sequence o1, o2,..., oN if the agent takes action sequence a1, a2,..., aN in some given history?” Littman et al. (2002) showed that predictions of this form are sufficient in that they can perfectly capture state and can be used to make any prediction, i.e., answer any question, about the system. In general, the number of predictions in the state vector grows linearly with the number of underlying or hidden system states and this can be too large for practical purposes. Of course, if one truly wants a

Predictive state representations (PSRs) are a recently-developed way to model discrete-time, controlled dynamical systems. We present and describe two algorithms for learning a PSR model: a Monte Carlo algorithm and a temporal difference (TD) algorithm. Both of these algorithms can learn models for systems without requiring a reset action as was needed by the previously available general PSR-model learning algorithm. We present empirical results that compare our two algorithms and also compare their performance with that of existing algorithms, including an EM algorithm for learning POMDP models.