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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מידע ביבליוגרפי

Principais autores:	Muhammad Hassan Jamal, Naila Naz, Muazzam A. Khan Khattak, Faisal Saeed, Saad Nasser Altamimi, Sultan Noman Qasem
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	IEEE 2023-01-01
סדרה:	IEEE Access
נושאים:	Deep learning (DL) machine learning (ML) multi-step attacks synthetic minority over-sampling technique (SMOTE) borderline SMOTE SMOTEENN
גישה מקוונת:	https://ieeexplore.ieee.org/document/10316288/
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!