TY  - CONF
ID  - SisLab3765
UR  - http://iscit2019.org/
A1  - Tran, Nghi Phu
A1  - Le, Huy Hoang
A1  - Nguyen, Ngoc Toan
A1  - Nguyen, Dai Tho
A1  - Nguyen, Ngoc Binh
Y1  - 2019/09//
N2  - 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%.
TI  - C500-CFG: A Novel Algorithm to Extract Control Flow-Based Features for IoT Malware Detection
SP  - 568
M2  - Ho Chi Minh City
AV  - public
EP  - 573
T2  - 19th International Symposium on Communications and Information Technologies (ISCIT 2019)
ER  -