Poster: PGPNet: Classify APT Malware Using Prediction-Guided Prototype Network

Authors:
Open Access
Publisher website
Google Scholar
Abstract
As the popularity of Advanced Persistent Threat (APT) grows, APT malware group classification has attracted more attention recently. However, most of previous methods use simple classifiers for group classification, ignoring the bias caused by the sparse number of revealed malware and the differences in functionality distribution of most groups. In this paper, we propose a Prediction-Guided Prototype Network (PGPNet) that could quickly adapt to new classification tasks with limited supervised samples based on the meta-learning architecture. Adding malware functionality classification as an auxiliary task is beneficial for feature learning, and the bias of distribution differences is eliminated by intervening the predicted results into the group classifier. Experimental results on a APT malware dataset show that PGPNet successfully exploits the contextual information and predictions of the auxiliary task and achieves state-of-the-art performance.

This publication has 11 references indexed in Scilit: