Combining Supervised and Unsupervised Learning for Zero-Day Malware Detection

Malware is one of the most damaging security threats facing the Internet today. Despite the burgeoning literature, accurate detection of malware remains an elusive and challenging endeavor due to the increasing usage of payload encryption and sophisticated obfuscation methods. Also, the large variety of malware classes coupled with their rapid proliferation and polymorphic capabilities and imperfections of real-world data (noise, missing values, etc) continue to hinder the use of more sophisticated detection algorithms. This paper presents a novel machine learning based framework to detect known and newly emerging malware at a high precision using layer 3 and layer 4 network traffic features. The framework leverages the accuracy of supervised classification in detecting known classes with the adaptability of unsupervised learning in detecting new classes. It also introduces a tree-based feature transformation to overcome issues due to imperfections of the data and to construct more informative features for the malware detection task. We demonstrate the effectiveness of the framework using real network data from a large Internet service provider.