| Title | A Robust Malware Detection System Using Deep Learning on API Calls |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Liu, Yingying, Wang, Yiwei |
| Conference Name | 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) |
| Date Published | mar |
| Keywords | API, API calls, API sequences, application program interfaces, BLSTM, Collaboration, component, composability, computer security, cuckoo sandbox, Deep Learning, feature extraction, invasive software, learning (artificial intelligence), Logic gates, Malware, malware detection, massive datasets, massive malware, neural nets, Neural networks, Object oriented modeling, policy-based governance, pubcrawl, redundant API, robust malware detection system, Sandboxing |
| Abstract | 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%. |
| DOI | 10.1109/ITNEC.2019.8728992 |
| Citation Key | liu_robust_2019 |
A Robust Malware Detection System Using Deep Learning on API Calls