%A Nghi Phu Tran
%A Huy Hoang Le
%A Ngoc Toan Nguyen
%A Dai Tho Nguyen
%A Ngoc Binh Nguyen
%T C500-CFG: A Novel Algorithm to Extract Control Flow-Based Features for IoT Malware Detection
%X Control flow-based features proposed by Ding, static characteristic extraction method, has the ability to detect malicious code with higher accuracy than traditional Text-based methods. However, this method resolved NP-hard problem in a graph, therefore it is not feasible with the large-size and highcomplexity programs. So, we propose the C500-CFG algorithm in Control flow-based features based on the idea of dynamic programming, solving Ding’s NP-hard problem by polynomial complexity O(N^2) algorithm, where N is the number of basic blocks in decompiled executable codes. Our algorithm is more efficient and more outstanding in detecting malware than Ding’s algorithm: fast processing time, allowing processing large files, using less memory and extracting more feature information. Applying our algorithms with IoT data sets gives outstanding results on 2 measures: Accuracy = 99.34%, F1-Score = 99.32%.
%C Ho Chi Minh City
%D 2019
%P 568-573
%L SisLab3765