A paper accepted in ACM CCS 2021

Title: MANIAC: A Man-Machine Collaborative System for Classifying Malware Author Groups
Author: Eujeanne Kim, Sung-Jun Park, Seokwoo Choi, Dong-Kyu Chae, Sang-Wook Kim
Abstract
In this demo, we show MANIAC, a MAN-machIne collaborative system for malware Author Classification. It is developed to fight a number of author groups who have been generating lots of new malwares by sharing source code within a group and exploiting evasive schemes such as polymorphism and metamorphism. Notably, MANIAC allows users to intervene in the model’s classification of malware authors with high uncertainty. It also provides effective interfaces and visualizations with users to achieve maximum classification accuracy with minimum human labor.