Malicious binaries have caused data and monetary loss to people, and these binaries keep evolving rapidly nowadays. With tons of new unknown attack binaries, one essential daily task for security analysts and researchers is to analyze and effectively identify malicious parts and report the critical behaviors within the binaries. While manual analysis is slow and ineffective, automated malware report generation is a long-term goal for malware analysts and researchers. This study moves one step toward the goal by identifying essential functions in malicious binaries to accelerate and even automate the analyzing process. We design and implement an expert system based on our proposed graph neural network called MalwareExpert. The system pinpoints the essential functions of an analyzed sample and visualizes the relationships between involved parts. We evaluate our proposed approach using executable binaries in the Windows operating system. The evaluation results show that our approach has a competitive detection performance (97.3% accuracy and 96.5% recall rate) compared to existing malware detection models. Moreover, it gives an intuitive and easy-to-understand explanation of the model predictions by visualizing and correlating essential functions. We compare the identified essential functions reported by our system against several expert-made malware analysis reports from multiple sources. Our qualitative and quantitative analyses show that the pinpointed functions indicate accurate directions. In the best case, the top 2% of functions reported from the system can cover all expert-annotated functions in three steps. We believe that the MalwareExpert system has shed light on automated program behavior analysis.

Working with a high-quality (complete and up-to-date) dataset is the key to building a good machine learning model, especially in security research areas. However, it is not easy to collect a good quality dataset for security research communities because of the sensitive property of most security datasets. We believe that having more contributors to share up-to-date samples would increase the quality of datasets. Therefore, this study aims to increase security dataset sharing for research communities by eliminating possible information leakage. We propose a dataset sharing model and the core algorithm, FeatureTransformer, which guarantees no sensitive information leakage from a shared dataset. FeatureTransformer transforms extracted raw features into intermediate features that conceal sensitive information. Meanwhile, models built from transformed features maintain similar performance compared to models built from the original raw features. We show the effectiveness of our model by evaluating FeatureTransformer with typical malware classification problems using (1) traditional machine learning classifiers and (2) neural network-based classifiers. The experiment results show that the models trained with transformed features merely suffer from 2.56% and 1.48% accuracy degradation on the investigated problems. It indicates that models validated by datasets processed by FeatureTransformer work well with the original raw (untransformed) datasets. We believe that our privacy-preserving model can stimulate dataset sharing and advance the development of machine learning approaches in solving security problems.