Sequence-based malware detection using a single-bidirectional graph embedding and multi-task learning framework
Jiale Luo,
Zhewngyu Zhang,
Jiesi Luo
et al.
Abstract:As an important part of malware detection and classification, sequence-based analysis can be integrated into dynamic detection system for real-time detection. This work presents a novel learning method for malware detection models that leverages advances in graph embedding for fusing the n-gram data into a one-hot feature space with different transmission directions. By capturing the information flow, our method finds a better feature representation for detection tasks with rely solely on sequence information.… Show more
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