%0 Conference Paper
%A Tran, Nghi Phu
%A Le, Huy Hoang
%A Nguyen, Ngoc Toan
%A Nguyen, Dai Tho
%A Nguyen, Ngoc Binh
%A VNU University of Engineering and Technology,
%A People’s Security Academy,
%B 19th International Symposium on Communications and Information Technologies (ISCIT 2019)
%C Ho Chi Minh City
%D 2019
%F SisLab:3765
%P 568-573
%T C500-CFG: A Novel Algorithm to Extract Control Flow-Based Features for IoT Malware Detection
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3765/
%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%.