Comparative Study of Two- and Multi-Class-Classification-Based Detection of Malicious Executables Using Soft Computing Techniques on Exhaustive Feature Set
Comparative Study of Two- and Multi-Class-Classification-Based Detection of Malicious Executables Using Soft Computing Techniques on Exhaustive Feature Set
Shina Sheen,R. Karthik,R. Anitha
2014 · DOI: 10.1007/978-81-322-1680-3_24
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TLDR
A comparative study of two- and multi-class-classification-based detection of malicious executables using soft computing techniques on exhaustive feature set is done and it is shown that AdaBoost, when combined with algorithms such as C4.5 and random forest with two-class classification, outperforms many other soft-computing-based techniques.