@inproceedings{SisLab3765,
       booktitle = {19th International Symposium on Communications and Information Technologies (ISCIT 2019)},
           month = {September},
           title = {C500-CFG: A Novel Algorithm to Extract Control Flow-Based Features for IoT Malware Detection},
          author = {Nghi Phu Tran and Huy Hoang Le and Ngoc Toan Nguyen and Dai Tho Nguyen and Ngoc Binh Nguyen},
            year = {2019},
           pages = {568--573},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3765/},
        abstract = {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\%.}
}