Improved email spam detection model with negative selection algorithm and particle swarm optimization
New email spam detection model based on negative selection algorithm and particle swarm optimization (NSAPSO) is implemented. A unique local best (pbest) particle is used as the optimum solution rather than global best (Gbest); since we do not have a unique optimal solution in our problem that will require to be determined using Gbest. 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 the reachability distance from the non-spam space and the local outlier factor of each particle within its neighbourhood to obtain the best features during detector generation.
email spam negative selection algorithm particle swarm optimization detector generation non-spam