Minami Someya (Institute of Information Security), Yuhei Otsubo (National Police Academy), Akira Otsuka (Institute of Information Security)

Malware classification facilitates static analysis, which is manually intensive but necessary work to understand the inner workings of unknown malware. Machine learning based approaches have been actively studied and have great potential. However, their drawback is that their models are considered black boxes and are challenging to explain their classification results and thus cannot provide patterns specific to malware. To address this problem, we propose FCGAT, the first malware classification method that provides interpretable classification reasons based on program functions. FCGAT applies natural language processing techniques to create function features and updates them to reflect the calling relationships between functions. Then, it applies attention mechanism to create malware feature by emphasizing the functions that are important for classification with attention weights. FCGAT provides an importance ranking of functions based on attention weights as an explanation. We evaluate the performance of FCGAT on two datasets. The results show that the F1-Scores are 98.15% and 98.18%, which are competitive with the cutting-edge methods. Furthermore, we examine how much the functions emphasized by FCGAT contribute to the classification. Surprisingly, our result show that only top 6 (average per sample) highly-weighted functions yield as much as 70% accuracy. We also show that these functions reflect the characteristics of malware by analyzing them. FCGAT can provide analysts with reliable explanations using a small number of functions. These explanations could bring various benefits, such as improved efficiency in malware analysis and comprehensive malware trend analysis.

View More Papers

Faster Secure Comparisons with Offline Phase for Efficient Private...

Florian Kerschbaum (University of Waterloo), Erik-Oliver Blass (Airbus), Rasoul Akhavan Mahdavi (University of Waterloo)

Read More

Why do Internet Devices Remain Vulnerable? A Survey with...

Tamara Bondar, Hala Assal, AbdelRahman Abdou (Carleton University)

Read More

The Walls Have Ears: Gauging Security Awareness in a...

Gokul Jayakrishnan, Vijayanand Banahatti, Sachin Lodha (TCS Research Tata Consultancy Services Ltd.)

Read More

Understanding MPU Usage in Microcontroller-based Systems in the Wild

Wei Zhou, Zhouqi Jiang (School of Cyber Science and Engineering, Huazhong University of Science and Technology), Le Guan (School of Computing, University of Georgia)

Read More