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Data Mining Methods for Detection of New Malicious Executables (0)
| Content Provider | CiteSeerX |
|---|---|
| Author | Zadok, Erez Schultz, Matthew G. Stolfo, Salvatore J. Eskin, Eleazar |
| Description | Proceedings of the IEEE Symposium on Security and Privacy IN PROCEEDINGS OF THE IEEE SYMPOSIUM ON SECURITY AND PRIVACY |
| 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. In this paper, 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 traditional signaturebased method, our method more than doubles the current detection rates for new malicious executables. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Data-mining Framework New Malicious Binary Serious Security Threat Today Data Set Malicious Executables New Malicious Program New Malicious Executables Detection Method Email Attachment Data Mining Method Unseen Malicious Executables Current Detection Rate Current Anti-virus System Attempt Traditional Signaturebased Method Serious Security Threat |
| Content Type | Text |
| Resource Type | Proceeding |
