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V-gram: Malware Detection Using Opcode Basic Blocks and Deep Learning
Seongmin Jeong, Hyeonseok Kim, Youngjae Kim, Myungkeun Yoon
http://doi.org/10.5626/JOK.2019.46.7.599
With the rapid increase in number of malwares, automatic detection based on machine learning becomes more important. Since the opcode sequence extracted from a malicious executable file is useful feature for malware detection, it is widely used as input data for machine learning through byte-based n-gram processing techniques. This study proposed a V-gram, a new data preprocessing technique for deep learning, which improves existing n-gram methods in terms of processing speed and storage space. V-gram can prevent unnecessary generation of meaningless input data from opcode sequences. It was verified that the V-gram is superior to the conventional n-gram method in terms of processing speed, storage space, and detection accuracy, through experiments conducted by collecting more than 64,000 normal and malicious code files.
A Hashing-Based Algorithm for Order-Preserving Multiple Pattern Matching
Munseong Kang, Sukhyeun Cho, Jeong Seop Sim
Given a text Tand a pattern P, the order-preserving pattern matching problem is to find all substrings in T which have the same relative orders as P. The order-preserving pattern matching problem has been studied in terms of finding some patterns affected by relative orders, not by their absolute values. Given a text T and a pattern set ℙ, the order-preserving multiple pattern matching problem is to find all substrings in T which have the same relative orders as any pattern in ℙ. In this paper, we present a hashing-based algorithm for the order-preserving multiple pattern matching problem.
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