Spam Detection Using Feature Selection and Parameters Optimization

By Sang Min Lee, Dong Seong Kim, Ji Ho Kim, Jong Sou Park. 2010

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, 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

1. Battista Biggio
2. Fabio Roli
3. Daniel S. Yeung
4. Patrick P. K. Chan
5. Fei Zhang
6. Shafii Muhammad Abdulhamid
7. Emmanuel Gbenga Dada
8. V. Varadharajan

In Computer Networks and Communications

1. Fabio Roli
2. Battista Biggio
3. Daniel S. Yeung
4. Patrick P. K. Chan
5. Fei Zhang
6. V. Varadharajan
7. Haruna Chiroma
8. Suhuai Luo

In Information Systems

1. Fabio Roli
2. Battista Biggio
3. Daniel S. Yeung
4. Patrick P. K. Chan
5. Fei Zhang
6. Shafii Muhammad Abdulhamid
7. Emmanuel Gbenga Dada
8. V. Varadharajan

Citations in the corpus

• Adversarial Feature Selection Against Evasion Attacks (2016, background): “require one to retrain the classification algorithm each time the feature subset is modified [51], [58].”
• Machine learning for email spam filtering: review, approaches and open research problems (2019, methodology): “Random forests have been successfully used to create a model for spam detection as explained by [43,125].”
• A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “A comparision and summary of ML models for spam detection over a decade. selection techniques obtained better accuracy than different similar methods [273], [276].”