Name
Abstract | ||
|---|---|---|
This work presents a novel system based on artificial immune system for spam detection. A relatively new machine learning method inspired by the human immune system called Artificial Immune System (AIS) has been emerging recently. This method is currently undergoing intense investigation and demonstration. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm (GA). SpamAssassin corpus is used in all our simulations. Spam is a serious universal problem which causes problems for almost all computer users. This issue affects not only normal users of the internet, but also causes problems for companies and organizations due to expensive costs in lost productivity, wasting users' time and network bandwidth. Many studies on spam indicate that it costs organizations billions of dollars annually. We introduce a GA assisted AIS in spam detection, and compare between two methods. Encouraging results were achieved when comparing to commercially available anti-spam software. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1142/S0218001411009123 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Artificial immune system, genetic algorithm, spam detection | Artificial immune system,Software,Bandwidth (signal processing),Artificial intelligence,Genetic algorithm,Machine learning,Mathematics,The Internet | Journal |
Volume | Issue | ISSN |
25 | 8 | 0218-0014 |
Citations | PageRank | References |
1 | 0.40 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raed Abu Zitar | 1 | 87 | 10.95 |
| Adel Hamdan | 2 | 19 | 1.32 |