In this article we use machine activity metrics to automatically distinguish between malicious and trusted portable executable software samples. The motivation stems from the growth of cyber attacks using techniques that have been employed to surreptitiously deploy Advanced Persistent Threats (APTs). APTs are becoming more sophisticated and able to obfuscate much of their identifiable features through encryption, custom code bases and in-memory execution. Our hypothesis is that we can produce a high degree of accuracy in distinguishing malicious from trusted samples using Machine Learning with features derived from the inescapable footprint left behind on a computer system during execution. This includes CPU, RAM, Swap use and network traffic at a count level of bytes and packets. These features are continuous and allow us to be more flexible with the classification of samples than discrete features such as API calls (which can also be obfuscated) that form the main feature of the extant literature. We use these continuous data and develop a novel classification method using Self Organizing Feature Maps to reduce over fitting during training through the ability to create unsupervised clusters of similar ‘behaviour’ that are subsequently used as features for classification, rather than using the raw data. We compare our method to a set of machine classification methods that have been applied in previous research and demonstrate an increase of between 7.24% and 25.68% in classification accuracy using our method and an unseen dataset over the range of other machine classification methods that have been applied in previous research
