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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Hauptverfasser: Yun Gao, Hirokazu Hasegawa, Yukiko Yamaguchi, Hajime Shimada
Format: Artigo
Sprache:Inglês
Veröffentlicht: IEEE 2022-01-01
Schriftenreihe:IEEE Access
Schlagworte:
Malware detection
machine learning
custom loss function
cost-sensitive learning
Online Zugang:https://ieeexplore.ieee.org/document/9766102/
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