Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

DOI10.1007/S10994-020-05870-YzbMath1497.68426OpenAlexW3011778916MaRDI QIDQ782462

Ewa Deelman, George Papadimitriou, Cong Wang, Anirban Mandal, Mariam Kiran

Publication date: 27 July 2020

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-020-05870-y

zbMATH Keywords

PCAautoencodersnetwork trafficisolation forest

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Network design and communication in computer systems (68M10)

Related Items (1)

Minimum cost‐compression risk in principal component analysis

Cites Work

• Unnamed Item
• Passive analysis of TCP anomalies
• Learning Deep Architectures for AI

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