Ant algorithms for discrete optimization
Artificial Life
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Immunotronics: Hardware Fault Tolerance Inspired by the Immune System
ICES '00 Proceedings of the Third International Conference on Evolvable Systems: From Biology to Hardware
Challenges of the Email Domain for Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An immunogenetic approach in chemical spectrum recognition
Advances in evolutionary computing
A Neural Network Based Approach to Automated E-Mail Classification
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Spam filters: bayes vs. chi-squared; letters vs. words
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
Filtering spam e-mail on a global scale
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Learning spam: simple techniques for freely-available software
ATEC '03 Proceedings of the annual conference on USENIX Annual Technical Conference
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Developing an immunity to spam
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Immunity from spam: an analysis of an artificial immune system for junk email detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
SDAI: An integral evaluation methodology for content-based spam filtering models
Expert Systems with Applications: An International Journal
Hybrid email spam detection model with negative selection algorithm and differential evolution
Engineering Applications of Artificial Intelligence
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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.