| Publisher | Florida Institute of Technology | ||
|---|---|---|---|
| Format | 58.5KB PDF | Date added | 01 Dec 2006 |
| Topics | Network Security, Artificial Intelligence, Security Management | ||
| Downloads | 29 | ||
As computers have become more ubiquitous and connected, their security has become a major concern. Attacks are more pervasive and diverse - they range from unsolicited email messages that can trick users in providing personal information to dangerous viruses that can erase data and shut down computer systems. Consequently, security breaches are not rare topics in the news. Conventional security software requires a lot of human effort to identity threats, extract characteristics from the threats, and encode the characteristics into software to detect the threats. This labor-intensive process can be more efficient by applying machine learning algorithms. As a result, a number of researchers have investigated various machine learning algorithms to detect attacks more efficiently and reliably.
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