Ensembles in adversarial classification for spam
By Deepak Chinavle, Pranam Kolari, Tim Oates, Timothy W. Finin. 2009
34 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yanjun Qi
- David E. Evans
- Weilin Xu
- Angelos Stavrou
- Charles Smutz
- Battista Biggio
- Danilo Vasconcellos Vargas
- Shashank Kotyan
In Information Systems
- Yanjun Qi
- Weilin Xu
- David E. Evans
- Charles Smutz
- Angelos Stavrou
- Battista Biggio
- Davide Maiorca
- Fabio Roli
Citations in the corpus
- Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers (2016, methodology): “argued that the adversarial problem is essentially concept drift, which is a well studied field in machine learning that considers data distributions which change over time [6].”
- When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors (2016, methodology): “To show the general applicability of our approach, we tested it against the Drebin Android malware detector where an uncertain prediction was correctly given to the majority of novel attacks.”