A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

A Comparison of Re-Sampling Techniques for Detection of Multi-Step Attacks on Deep Learning Models

The increasing dependence on data analytics and artificial intelligence (AI) methodologies across various domains has prompted the emergence of apprehensions over data security and integrity. There exists a consensus among scholars and experts that the identification and mitigation of Multi-step att...

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Bibliographic Details
Main Authors: Muhammad Hassan Jamal, Naila Naz, Muazzam A. Khan Khattak, Faisal Saeed, Saad Nasser Altamimi, Sultan Noman Qasem
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Deep learning (DL)
machine learning (ML)
multi-step attacks
synthetic minority over-sampling technique (SMOTE)
borderline SMOTE
SMOTEENN
Online Access:https://ieeexplore.ieee.org/document/10316288/

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