Application of genetic optimized artificial immune system and neural networks in spam detection

2011 ◽  
Vol 11 (4) ◽  
pp. 3827-3845 ◽  
Author(s):  
Adel Hamdan Mohammad ◽  
Raed Abu Zitar
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Spam Detection ◽  
Artificial Immune

THE MODEL AND ALGORITHM ARTIFICIAL IMMUNE SYSTEM

2016 ◽  
Vol 8 (3) ◽  
pp. 5-10
Author(s):  
Астахова ◽  
I. Astakhova ◽  
Ушаков ◽  
S. Ushakov
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Program Complex ◽  
Immune Systems ◽  
The Creation

In particular, models had only one type of cages , they applied V-lymphocytes. The distribution and a decentralization were the second feature for using artificial immune systems. This article is devoted to creation the artificial immune system (AIS), the creation model and algorithm of IIS is considered. The model for realization of a problem is consid-ered. Accuracy of calculations is compared to other methods, especially to neural networks. The structure of a program complex is described.

SPAM DETECTION USING GENETIC ASSISTED ARTIFICIAL IMMUNE SYSTEM

2011 ◽  
Vol 25 (08) ◽  
pp. 1275-1295 ◽  
Author(s):  
RAED ABU ZITAR ◽  
ADEL HAMDAN MOHAMMAD
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Spam Detection ◽  
Artificial Immune ◽  
Human Immune System ◽  
Network Bandwidth ◽  
Lost Productivity ◽  
Human Immune ◽  
Almost All ◽  
New Machine

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.

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