Static Feature Selection for IoT Malware Detection
DOI:
https://doi.org/10.54654/isj.v1i15.844Keywords:
feature selection, opcode, IoT malware, malware detection, machine learningTóm tắt
Abstract— Our world has recently witnessed the explosive growth of IoT networks as one of the pillars of the 4th industrial revolution. Malware on IoT devices also grows accordingly in number and sophisticated techniques. Therefore, it is necessary to come up with more efficient approaches to IoT malware detection with machine learning models that can be used in solutions using limited resources. In this paper, we study and evaluate the efficiency of using a weight of term frequency– inverse document frequency model in feature selection method combined with an effective machine learning model in IoT malware detection based on opcode sequence features. We performed experiments on a MIPS ELF dataset that included 4,511 malicious samples with main four classes and 4,393 benign programs. Experiment results show that our proposed method has very good performance on the above dataset with detection and classification accuracy which are 99.8% and 95.8% respectively while the models only use 20 opcodes that have the highest weight values.
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