A combined negative selection algorithm–particle swarm optimization for an email spam detection system

• New email spam detection model based on negative selection algorithm and particle swarm optimization (NSA–PSO) is implemented.
• Random detector generation is replaced with particle swarm optimization; distance measure and threshold value were studied to select distinctive features for spam detection.
• Local outlier factor (LOF) is implemented as fitness function to calculate reachability distance from non-spam space and the local outlier factor of each particle within its neighborhood to obtain the best features during detector generation.
• The proposed NSA–PSO model is use to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection.

Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a real-time protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA–PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA–PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection.

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