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- W4377082023 abstract "Malicious JavaScript detection using machine learning models has shown many great results over the years. However, real-world data only has a small fraction of malicious JavaScript, making it an imbalanced dataset. Many of the previous techniques ignore most of the benign samples and focus on training a machine learning model with a balanced dataset. This paper proposes a Doc2Vec-based filter model that can quickly classify JavaScript malware using Natural Language Processing (NLP) and feature re-sampling. The feature of the JavaScript file will be converted into vector form and used to train the classifiers. Doc2Vec, a NLP model used for documents is used to create feature vectors from the datasets. In this paper, the total features of the benign samples will be reduced using a combination of word vector and clustering model. Random seed oversampling will be used to generate new training malicious data based on the original training dataset. We evaluate our models with a dataset of over 30,000 samples obtained from top popular websites, PhishTank, and GitHub. The experimental result shows that the best f1-score achieves at 0.99 with the MLP classifier." @default.
- W4377082023 created "2023-05-20" @default.
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- W4377082023 date "2023-01-01" @default.
- W4377082023 modified "2023-09-27" @default.
- W4377082023 title "Malicious JavaScript Detection Based on AST Analysis and Key Feature Re-sampling in Realistic Environments" @default.
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- W4377082023 doi "https://doi.org/10.1007/978-3-031-33017-9_15" @default.
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