relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3998/
title: An Efficient Algorithm to Extract Control Flow-based Features for IoT Malware Detection
creator: Tran, Nghi Phu
creator: Nguyen, Dai Tho
creator: Le, Huy Hoang
creator: Nguyen, Ngoc Toan
creator: Nguyen, Ngoc Binh
subject: Information Technology (IT)
subject: ISI-indexed journals
description: 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%.
publisher: Oxford University Press
date: 2020
type: Article
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3998/1/CJ.pdf
identifier:   Tran, Nghi Phu and Nguyen, Dai Tho and Le, Huy Hoang and Nguyen, Ngoc Toan and Nguyen, Ngoc Binh  (2020) An Efficient Algorithm to Extract Control Flow-based Features for IoT Malware Detection.  The Computer Journal .    ISSN 0010-4620    (In Press)  
relation: https://academic.oup.com/comjnl