A combined negative selection algorithmparticle swarm optimization for an email spam detection system
New email spam detection model based on negative selection algorithm and particle swarm optimization (NSAPSO) 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 NSAPSO model is use to differentiate between spam and non-spam in a network that is developed based on a clientserver network for spam detection.
negative selection algorithm particle swarm optimization differential evolution spam detectors