Obfuscating Function Call Topography to Test Structural Malware Detection against Evasion Attacks

The incredible popularity of the Android mobile operating system has resulted in a massive influx of malicious applications for the platform. This malware can come from a number of sources as Google allows the installation of Android App Packages (APKs) from third parties. Even within its own Google Play storefront, however, malicious software can be found. One type of approach to identify malware focuses on the structural properties of the function call graphs (FCGs) extracted from APKs. The aim of this research work is to test the robustness of one example method in this category, named the ACTS (App topologiCal signature through graphleT Sampling) method. By extracting graphlet statistics from a FCG, the ACTS approach is able to efficiently differentiate between benign app samples and malware with good accuracy. In this work, we obfuscate the FCG of malware in several ways, and test the ACTs method against these evasion attacks. The statistical results of running ACTS against unmodified real malware samples is compared with the results of ACTS running against obfuscated versions of those same apps

A Hierarchical Temporal Memory Sequence Classifier for Streaming Data

Real-world data streams often contain concept drift and noise. Additionally, it is often the case that due to their very nature, these real-world data streams also include temporal dependencies between data. Classifying data streams with one or more of these characteristics is exceptionally challenging. Classification of data within data streams is currently the primary focus of research efforts in many fields (i.e., intrusion detection, data mining, machine learning). Hierarchical Temporal Memory (HTM) is a type of sequence memory that exhibits some of the predictive and anomaly detection properties of the neocortex. HTM algorithms conduct training through exposure to a stream of sensory data and are thus suited for continuous online learning. This research developed an HTM sequence classifier aimed at classifying streaming data, which contained concept drift, noise, and temporal dependencies. The HTM sequence classifier was fed both artificial and real-world data streams and evaluated using the prequential evaluation method. Cost measures for accuracy, CPU-time, and RAM usage were calculated for each data stream and compared against a variety of modern classifiers (e.g., Accuracy Weighted Ensemble, Adaptive Random Forest, Dynamic Weighted Majority, Leverage Bagging, Online Boosting ensemble, and Very Fast Decision Tree). The HTM sequence classifier performed well when the data streams contained concept drift, noise, and temporal dependencies, but was not the most suitable classifier of those compared against when provided data streams did not include temporal dependencies. Finally, this research explored the suitability of the HTM sequence classifier for detecting stalling code within evasive malware. The results were promising as they showed the HTM sequence classifier capable of predicting coding sequences of an executable file by learning the sequence patterns of the x86 EFLAGs register. The HTM classifier plotted these predictions in a cardiogram-like graph for quick analysis by reverse engineers of malware. This research highlights the potential of HTM technology for application in online classification problems and the detection of evasive malware

Evaluation Methodologies in Software Protection Research

Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks