Analysis of N-Way K-Shot Malware Detection Using Few-Shot Learning

Kwok Tai Chui, Brij B. Gupta, Lap Kei Lee, Miguel Torres-Ruiz

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Solving machine learning problems with small-scale training datasets becomes an emergent research area to fill the opposite end of big data applications. Attention is drawn to malware detection using few-shot learning, which is typically formulated as N-way K-shot problems. The aims are to reduce the effort in data collection, learn the rare cases, reduce the model complexity, and increase the accuracy of the detection model. The performance of the malware detection model is analyzed with the variation in the number of ways and the number of shots. This facilitates the understanding on the design and formulation of three algorithms namely relation network, prototypical network, and relation network for N-way K-shot problems. Two benchmark datasets are selected for the performance evaluation. Results reveal the general characteristics of the performance of malware detection model with fixed ways and varying shots, and varying ways and fixed shots based on the trends of results of 30 scenarios.
Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Number of pages12
Publication statusPublished - 2023

Publication series

NameLecture Notes in Networks and Systems
Volume599 LNNS


  • Few-shot learning
  • Imbalanced dataset
  • Malware detection
  • N-way K-shot
  • Small-scale dataset


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