An Efficient DenseNet-Based Deep Learning Model for Malware Detection.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101243874 Publication Model: Electronic Cited Medium: Internet ISSN: 1099-4300 (Electronic) Linking ISSN: 10994300 NLM ISO Abbreviation: Entropy (Basel) Subsets: PubMed not MEDLINE

Original Publication: Basel, Switzerland : MDPI, 1999-

Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover new ways to evade these defense techniques. Traditional static and dynamic analysis methods are ineffective in identifying new malware and pose high overhead in terms of memory and time. Typical machine learning approaches that train a classifier based on handcrafted features are also not sufficiently potent against these evasive techniques and require more efforts due to feature-engineering. Recent malware detectors indicate performance degradation due to class imbalance in malware datasets. To resolve these challenges, this work adopts a visualization-based method, where malware binaries are depicted as two-dimensional images and classified by a deep learning model. We propose an efficient malware detection system based on deep learning. The system uses a reweighted class-balanced loss function in the final classification layer of the DenseNet model to achieve significant performance improvements in classifying malware by handling imbalanced data issues. Comprehensive experiments performed on four benchmark malware datasets show that the proposed approach can detect new malware samples with higher accuracy (98.23% for the Malimg dataset, 98.46% for the BIG 2015 dataset, 98.21% for the MaleVis dataset, and 89.48% for the unseen Malicia dataset) and reduced false-positive rates when compared with conventional malware mitigation techniques while maintaining low computational time. The proposed malware detection solution is also reliable and effective against obfuscation attacks.

ScientificWorldJournal. 2014;2014:132713. (PMID: 25133202)
Nature. 2015 May 28;521(7553):436-44. (PMID: 26017442)
Sensors (Basel). 2020 Dec 08;20(24):. (PMID: 33302430)

Keywords: cybersecurity; deep learning; densely connected convolutional network; malware detection; malware visualization

Date Created: 20210403 Latest Revision: 20210413

20240829

PMC7998822

10.3390/e23030344

33804035