Trend of Malware Detection Using Deep Learning
Yoon-seon Lee, Jae-Ung Lee, Wooyoung Soh · 2018
According to recent security trends, there are more variations of existing malware than new type malware. These malwares are causing a lot of damage, such as encrypting users ' data to leak personal information, delete data, and make financial demands. Although many studies are being conducted to analyze malwares in order to respond to the rapidly increasing malware with such malicious purpose, current malware analysis methods are for obfuscation, virtual environment bypass, etc. To overcome these difficulties, Deep Learning method that analyzes and utilizes harmful codes is receiving spotlight recently. Therefore, this thesis introduces trends on how to analyze malware based on Deep Learning.