Application of genetic optimized artificial immune system and neural networks in spam detection
Spam is a serious universal problem which causes problems for almost all computer users. This issue not only affects normal users of the internet, but also causes a big problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting users time and network bandwidth. There are many studies on spam indicates that spam costs organizations billions of dollars yearly. This work presents a lot of modification on a machine learning method inspired by the human immune system called artificial immune system (AIS) which is a new emerging method that still needs more investigations and demonstrations. Core modifications were applied on the standard AIS with the aid of the Genetic Algorithm (GA). Also Artificial Neural Network (ANN) for spam detection is applied in a new manner. SpamAssassin corpus is used in all our simulations. In standard AIS several user defined parameters are used such as culling of old lymphocytes. Genetic optimized AIS is used to present culling time instead of using user defined value. Also, a new idea to check antibodies in AIS is introduced. This would make the system able to accept types of messages that were previously considered as spam. The idea is accomplished by introducing a new issue which we call rebuild time. Moreover, an adaptive weighting of lymphocytes is used to modify selection opportunities for different gene fragments. In this work also, core modifications on ANN neurons are applied; these modifications allow neurons to be changed over time replacing useless layers. This approach is called Continuous Learning Approach Artificial Neural Network, CLA_ANN. The final results are compared and analyzed. Results show that both systems, optimized spam detection using GA and spam detection using ANN, achieved promising scores comparable to standard AIS and other known methods.
immune system spam genetic algorithm