Email Worm Detection Using Naïve Bayes and Support Vector Machine
Email worm, as the name implies, spreads through infected email messages. The worm may be carried by attachment, or the email may contain links to an infected website. When the user opens the attachment, or clicks the link, the host is immediately infected. Email worms use the vulnerability of the email software of the host machine and sends infected emails to the addresses stored in the address book. In this way, new machines get infected. Examples of email worms are “W32.mydoom.M@mm”, “W32.Zafi.d”, “W32.LoveGate.w”, “W32.Mytob.c”, and so on. Worms do a lot of harm to computers and people. They can clog the network traffic, cause damage to the system and make the system unstable or even unusable.
KeywordsSupport Vector Machine Cross Validation False Negative Rate Network Traffic Support Vector Machine Classifier
- 1.Martin, S., Sewani, A., Nelson, B., Joseph, K.C.A.D.: A Two-Layer Approach for Novel Email Worm Detection, http://www.cs.berkeley.edu/~anil/papers/SRUTI_submitted.pdf