Application of Hybrid Machine Learning to Detect and Remove Malware


  • Richard Ruiqi Yang University of Wyoming
  • Victor Kang College of Literature, Science and the Arts, University of Michigan Ann Arbor, United States
  • Sami Albouq School of Engineering and Computer Science, Oakland University, United States
  • Mohamed A. Zohdy School of Engineering and Computer Science, Oakland University, United States;



machine learning, data mining, self-organizing feature map algorithm, malware detection


Anti-malware software traditionally employ methods of signature-based and heuristic-based detection. These detection systems need to be manually updated with new behaviors to detect new, unknown, or adapted malware. Our goal is to create a new malware detection solution that will serve three purposes: to automatically identify and classify unknown files on a spectrum of malware severity; to introduce a hybrid machine learning approach to detect modified malware traces; and to increase the accuracy of detection results. Our solution is accomplished through the use of data mining and machine learning concepts and algorithms. We perform two types of data mining on samples, extracting n-grams and PE features that are used for our machine learning environment. We also introduce a new hybrid learning approach that utilizes both supervised and unsupervised machine learning in a two-layer protocol. A supervised algorithm is applied to classify if a file is considered malware or benign. The files classified as malware will then be categorized and then assigned on a severity spectrum using the SOFM unsupervised algorithm.

Author Biography

Richard Ruiqi Yang, University of Wyoming

Department of Computer Science | Department of Electrical and Computer Engineering

College of Engineering and Applied Sciences, University of Wyoming


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How to Cite

Yang, R. R., Kang, V., Albouq, S., & Zohdy, M. A. (2015). Application of Hybrid Machine Learning to Detect and Remove Malware. Transactions on Engineering and Computing Sciences, 3(4), 16.