Jasmine: A new Active Learning approach to combat cybercrime
نویسندگان
چکیده
One of the reasons that deployment network intrusion detection methods falls short is lack realistic labeled datasets, which makes it challenging to develop and compare techniques. It caused by large amounts effort takes for a cyber expert classify connections. This has raised need learn from both unlabeled data observations are best present human expert. Hence, Active Learning (AL) interest. In this paper, we propose new hybrid AL method called Jasmine. Firstly, uses uncertainty score anomaly determine how suitable each observation querying, i.e., likely enhance classification. Secondly, Jasmine introduces dynamic updating. allows model adjust balance between querying uncertain, anomalous randomly selected observations. To end, able query strategy during labeling process. in contrast other cybersecurity all have static, predetermined functions. We show updating, therefore Jasmine, consistently obtain good more robust results than only uncertainties, anomalies or fixed combination two. • novel detection. queries can dynamically optimize these types. first with performs better existing static methods.
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ژورنال
عنوان ژورنال: Machine learning with applications
سال: 2022
ISSN: ['2666-8270']
DOI: https://doi.org/10.1016/j.mlwa.2022.100351