基于API调用的深度学习鲁棒恶意软件检测系统

Yingying Liu, Yiwei Wang
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引用次数: 17

摘要

随着技术的发展,大量的恶意软件成为当前计算机安全面临的主要挑战。在我们的工作中,我们使用API调用的深度学习实现了一个恶意软件检测系统。利用杜鹃沙盒的方法,提取了恶意程序的API调用序列。通过对冗余API调用进行过滤和排序,提取出有效的API序列。对比GRU、BGRU、LSTM和SimpleRNN,我们在包含21378个样本的海量数据集上对BLSTM进行了评估。实验结果表明,BLSTM具有最佳的恶意软件检测性能,准确率达到97.85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Robust Malware Detection System Using Deep Learning on API Calls
With the development of technology, the massive malware become the major challenge to current computer security. In our work, we implemented a malware detection system using deep learning on API calls. By means of cuckoo sandbox, we extracted the API calls sequence of malicious programs. Through filtering and ordering the redundant API calls, we extracted the valid API sequences. Compared with GRU, BGRU, LSTM and SimpleRNN, we evaluated the BLSTM on the massive datasets including 21,378 samples. The experimental results demonstrate that BLSTM has the best performance for malware detection, reaching the accuracy of 97.85%.
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