Malware classification using deep learning methods
作者： Bugra Cakir , Erdogan Dogdu
关键词: Task (computing) 、 Malware 、 Feature extraction 、 Supervised learning 、 Opcode 、 Deep learning 、 Gradient boosting 、 Word2vec 、 Artificial intelligence 、 Computer science 、 Machine learning
摘要: Malware, short for Malicious Software, is growing continuously in numbers and sophistication as our digital world continuous to grow. It a very serious problem many efforts are devoted malware detection today's cybersecurity world. Many machine learning algorithms used the automatic of recent years. Most recently, deep being with better performance. Deep models shown work much analysis long sequences system calls. In this paper shallow learning-based feature extraction method (word2vec) representing any given based on its opcodes. Gradient Boosting algorithm classification task. Then, k-fold cross-validation validate model performance without sacrificing validation split. Evaluation results show up 96% accuracy limited sample data.
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