Malware Detection Using LightGBM With a Custom Logistic Loss Function

The increased spread of malicious software (malware) through the internet remains a serious threat. Malware authors use obfuscation and deformation techniques to generate new types than can evade traditional detection methods. Hence, it is widely expected that machine learning methods can classify m...

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Bibliografische gegevens
Hoofdauteurs: Yun Gao, Hirokazu Hasegawa, Yukiko Yamaguchi, Hajime Shimada
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: IEEE 2022-01-01
Reeks:IEEE Access
Onderwerpen:
Malware detection
machine learning
custom loss function
cost-sensitive learning
Online toegang:https://ieeexplore.ieee.org/document/9766102/
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