Static Malware Detection Using Stacked Bi-Directional LSTM

Demirci, Deniz
The recent proliferation in the use of the Internet and personal computers has made it easier for cybercriminals to expose Internet users to widespread and damaging threats. In order protect the end users against such threats, a security system must be proactive. It needs to detect malicious files or executables before reaching the end-user. To create an efficient and low-cost malware detection mechanism, in the present study, we propose stacked bidirectional long short-term memory (Stacked BiLSTM) based deep learning (DL) language model for detecting malicious code. We developed language models using assembly instructions from .text sections of malicious and benign Portable Executable (PE) files. We created our first dataset from assembly instructions obtained from static analysis of the PE files. The dataset was composed of text documents, and it was used in Document Level Analysis Model (DLAM). By splitting the first dataset into single instructions, we obtained the second dataset, which was then used in a Sentence Level Analysis Model (SLAM). We treated each instruction as a sentence, and .text sections as documents. We labeled each document and sentence by their corresponding malicious and benign tags. The experiments showed that the Document Level Analysis Model (DLAM), and the Sentence Level Analysis Model (SLAM) achieved 98,3% and 70.4% F1 scores, respectively.


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Citation Formats
D. Demirci, “Static Malware Detection Using Stacked Bi-Directional LSTM,” M.S. - Master of Science, Middle East Technical University, 2021.