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A novel approach for early malware detection
Author(s) -
Sharma Anshul,
Kumar Singh Sanjay
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3968
Subject(s) - computer science , probabilistic logic , particle swarm optimization , data mining , malware , time series , machine learning , artificial intelligence , series (stratigraphy) , early stopping , artificial neural network , paleontology , biology , operating system
Early classification of time series is valuable in many real‐world applications such as early disease prediction, early disaster prediction, and patient monitoring where data are generated over time. The main objective of early classification is to provide a reliable class prediction earliest in time. In general, whenever the early prediction time improves, the prediction accuracy decreases. Thus, the trade‐off between earliness and accuracy needs to be addressed. In this article, we proposed an optimization‐based early classification model for time series data using early stopping rules (ESRs) and a series of probabilistic classifiers. ESRs are developed through particle swarm optimization by minimizing the well‐defined cost function that considers the missclassification cost and delaying decision cost simultaneously. The experimental results on 30 standard datasets demonstrate good performance for early classification in comparison to state of the art methods. Also, the proposed model is tested for early malware detection on a real dataset and shows decent performance by balancing the accuracy and earliness.