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 (NSA–PSO) 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.

The adaptive nature of unsolicited email by the use of huge mailing tools prompts the need for spam detection. Implementation of different spam detection methods based on machine learning techniques was proposed to solve the problem of numerous email spam ravaging the system. Previous algorithm used in email spam detection compares each email message with spam and non-spam data before generating detectors while our proposed system inspired by the artificial immune system model with the adaptive nature of negative selection algorithm uses special features to generate detectors to cover the spam space. To cope with the trend of email spam, a novel model that improves the random generation of a detector in negative selection algorithm (NSA) with the use of stochastic distribution to model the data point using particle swarm optimization (PSO) was implemented. Local outlier factor is introduced as the fitness function to determine the local best (Pbest) of the candidate detector that gives the optimum solution. Distance measure is employed to enhance the distinctiveness between the non-spam and spam candidate detector. The detector generation process was terminated when the expected spam coverage is reached. The theoretical analysis and the experimental result show that the detection rate of NSA–PSO is higher than the standard negative selection algorithm. Accuracy for 2000 generated detectors with threshold value of 0.4 was compared. Negative selection algorithm is 68.86% and the proposed hybrid negative selection algorithm with particle swarm optimization is 91.22%.

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