TY  - INPR
ID  - SisLab3998
UR  - https://academic.oup.com/comjnl
A1  - Tran, Nghi Phu
A1  - Nguyen, Dai Tho
A1  - Le, Huy Hoang
A1  - Nguyen, Ngoc Toan
A1  - Nguyen, Ngoc Binh
Y1  - 2020///
N2  - Control flow-based feature extraction method has the ability to detect malicious code with higher accuracy than traditional text-based methods. Unfortunately, this method has been encountering with the NP-hard problem, which is infeasible for the large-sized and high-complexity programs. To tackle this, we propose a control flow-based features extraction dynamic programming algorithm (CFD) for fast extraction of control flow-based features with polynomial time O(N^2), where N is the number of basic blocks in decompiled executable codes. From the experimental results, it is demonstrated that the proposed algorithm is more efficient and effective in detecting malware than the existing ones. Applying our algorithm to an IoT dataset gives better results on 3 measures: Accuracy (AC) = 99.05%, False Positive Rate (FPR) = 1.31% and False Negative Rate (FNR) = 0.66%.
PB  - Oxford University Press
JF  - The Computer Journal
SN  - 0010-4620
TI  - An Efficient Algorithm to Extract Control Flow-based Features for IoT Malware Detection
AV  - public
ER  -