By a News Reporter-Staff News Editor at Computers, Networks & Communications -- Investigators discuss new findings in Information Technology - Information Systems. According to news reporting from Morgantown, West Virginia, by VerticalNews journalists, research stated, “With computers and the Internet being essential in everyday life, malware poses serious and evolving threats to their security, making the detection of malware of utmost concern. Accordingly, there have been many researches on intelligent malware detection by applying data mining and machine learning techniques.”
Financial support for this research came from U.S. National Science Foundation.
The news correspondents obtained a quote from the research from West Virginia University, “Though great results have been achieved with these methods, most of them are built on shallow learning architectures. Due to its superior ability in feature learning through multilayer deep architecture, deep learning is starting to be leveraged in industrial and academic research for different applications. In this paper, based on the Windows application programming interface calls extracted from the portable executable files, we study how a deep learning architecture can be designed for intelligent malware detection. We propose a heterogeneous deep learning framework composed of an AutoEncoder stacked up with multilayer restricted Boltzmann machines and a layer of associative memory to detect newly unknown malware. The proposed deep learning model performs as a greedy layer-wise training operation for unsupervised feature learning, followed by supervised parameter fine-tuning. Different from the existing works which only made use of the files with class labels (either malicious or benign) during the training phase, we utilize both labeled and unlabeled file samples to pre-train multiple layers in the heterogeneous deep learning framework from bottom to up for feature learning. A comprehensive experimental study on a real and large file collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that our proposed deep learning framework can further improve the overall performance in malware detection compared with traditional shallow learning methods, deep learning methods with homogeneous framework, and other existing anti-malware scanners.”
According to the news reporters, the research concluded: “The proposed heterogeneous deep learning framework can also be readily applied to other malware detection tasks.”
For more information on this research see: DeepAM: a heterogeneous deep learning framework for intelligent malware detection. Knowledge and Information Systems , 2018;54(2):265-285. Knowledge and Information Systems can be contacted at: Springer London Ltd, 236 Grays Inn Rd, 6TH Floor, London WC1X 8HL, England. (Springer - www.springer.com; Knowledge and Information Systems - http://www.springerlink.com/content/0219-1377/)
Our news journalists report that additional information may be obtained by contacting Y.F. Ye, West Virginia Univ, Dept. of Comp Sci & Elect Engn, Morgantown, WV 26506, United States. Additional authors for this research include L.W. Chen, S.F. Hou, W. Hardy and X. Li.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s10115-017-1058-9. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-02-15), Reports from West Virginia University Highlight Recent Findings in Information Systems (DeepAM: a heterogeneous deep learning framework for intelligent malware detection), Computers, Networks & Communications, 457, ISSN: 1944-1568, BUTTER® ID: 015146716
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