Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
Learning spam: simple techniques for freely-available software
ATEC '03 Proceedings of the annual conference on USENIX Annual Technical Conference
Revisiting LISYS: parameters and normal behavior
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Engineering Applications of Artificial Intelligence
Intelligent Detection Approaches for Spam
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Engineering Applications of Artificial Intelligence
Advances in Engineering Software
Anti-spam legislation: An analysis of laws and their effectiveness
Information and Communications Technology Law
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
A Novel Spam Email Detection System Based on Negative Selection
ICCIT '09 Proceedings of the 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology
Developing an immunity to spam
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A scalable intelligent non-content-based spam-filtering framework
Expert Systems with Applications: An International Journal
Immunity-based model for malicious code detection
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
A survey and experimental evaluation of image spam filtering techniques
Pattern Recognition Letters
Efficient and effective spam filtering and re-ranking for large web datasets
Information Retrieval
An immunological filter for spam
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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
SDAI: An integral evaluation methodology for content-based spam filtering models
Expert Systems with Applications: An International Journal
Comparison of evolutionary-based optimization algorithms for structural design optimization
Engineering Applications of Artificial Intelligence
Optimising anti-spam filters with evolutionary algorithms
Expert Systems with Applications: An International Journal
A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing
Applied Soft Computing
Advances in Fuzzy Systems - Special issue on High Performance Fuzzy Systems for Real World Problems
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Email spam is an increasing problem that not only affects normal users of internet but also causes a major problem for companies and organizations. Earlier techniques have been impaired by the adaptive nature of unsolicited email spam. Inspired by adaptive algorithm, this paper introduces a modified machine learning technique of the human immune system called negative selection algorithm (NSA). A local selection differential evolution (DE) generates detectors at the random detector generation phase of NSA; code named NSA-DE. Local outlier factor (LOF) is implemented as fitness function to maximize the distance of generated spam detectors from the non-spam space. The problem of overlapping detectors is also solved by calculating the minimum and maximum distance of two overlapped detectors in the spam space. From the experiments, the results show that the detection accuracy of NSA-DE is 83.06% while the standard negative selection algorithm is 68.86% at 7000 generated detectors.