Data mining methods for detection of new malicious executables
Columbia University · Stony Brook University · +1 more institution
Abstract
A serious security threat today is malicious executables, especially new, unseen malicious executables often arriving as email attachments. These new malicious executables are created at the rate of thousands every year and pose a serious security threat. Current anti-virus systems attempt to detect these new malicious programs with heuristics generated by hand. This approach is costly and oftentimes ineffective. We present a data mining framework that detects new, previously unseen malicious executables accurately and automatically. The data mining framework automatically found patterns in our data set and used these patterns to detect a set of new malicious binaries. Comparing our detection methods with a…
Citation impact
- FWCI
- 20.17
- Percentile
- 100%
- References
- 27
Authors
4Topics & keywords
- Executable
- Computer science
- Heuristics
- Set (abstract data type)
- Malware
- Data mining
- Computer security
- Operating system
- Peace, Justice and strong institutions