@inproceedings{084c3245e4e34104b627c18e361cbe15,
title = "Analysis of N-Way K-Shot Malware Detection Using Few-Shot Learning",
abstract = "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.",
keywords = "Few-shot learning, Imbalanced dataset, Malware detection, N-way K-shot, Small-scale dataset",
author = "Chui, {Kwok Tai} and Gupta, {Brij B.} and Lee, {Lap Kei} and Miguel Torres-Ruiz",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Cyber Security, Privacy and Networking, ICSPN 2022 ; Conference date: 09-09-2021 Through 11-09-2021",
year = "2023",
doi = "10.1007/978-3-031-22018-0_4",
language = "Ingl{\'e}s",
isbn = "9783031220173",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "33--44",
editor = "Nadia Nedjah and {Mart{\'i}nez P{\'e}rez}, Gregorio and B.B. Gupta",
booktitle = "International Conference on Cyber Security, Privacy and Networking, ICSPN 2022",
address = "Alemania",
}