@Article{cmc.2020.010091,
AUTHOR = {Chunlai Du, Shenghui Liu, Lei Si, Yanhui Guo, Tong Jin},
TITLE = {Using Object Detection Network for Malware Detection and  Identification in Network Traffic Packets},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1785--1796},
URL = {http://www.techscience.com/cmc/v64n3/39459},
ISSN = {1546-2226},
ABSTRACT = {In recent years, the number of exposed vulnerabilities has grown rapidly and
more and more attacks occurred to intrude on the target computers using these 
vulnerabilities such as different malware. Malware detection has attracted more attention 
and still faces severe challenges. As malware detection based traditional machine 
learning relies on exports’ experience to design efficient features to distinguish different 
malware, it causes bottleneck on feature engineer and is also time-consuming to find 
efficient features. Due to its promising ability in automatically proposing and selecting 
significant features, deep learning has gradually become a research hotspot. In this paper, 
aiming to detect the malicious payload and identify their categories with high accuracy, 
we proposed a packet-based malicious payload detection and identification algorithm 
based on object detection deep learning network. A dataset of malicious payload on code 
execution vulnerability has been constructed under the Metasploit framework and used to 
evaluate the performance of the proposed malware detection and identification algorithm. 
The experimental results demonstrated that the proposed object detection network can 
efficiently find and identify malicious payloads with high accuracy.},
DOI = {10.32604/cmc.2020.010091}
}