Advanced Spam Email Detection using Machine Learning and Bio-Inspired Meta-Heuristics Algorithms
Keywords:Convolution neural network, Machine Learning, Genetic Algorithm, Particle Swarm Optimization, Spam detection
Modern methods for precise detection and mitigation of spam emails are required since they continue to be a ubiquitous and changing menace. By combining machine learning and metaheuristics algorithms that are bio-inspired, we provide a novel method for detecting spam emails in this study. The conventional rule-based and content-based approaches are not always able to keep up with spammers' constantly evolving strategies. In order to overcome this difficulty, we suggest a hybrid model that makes use of both the advantages of machine learning and bio-inspired algorithms. Our approach makes use of a broad range of features gleaned from email headers, text, attachments, and sender behaviour. The accuracy of the detection is improved by this component, which catches complex patterns and relationships within the data. The categorization method is then optimized by using bio-inspired metaheuristics algorithms like particle swarm optimization (PSO) or genetic algorithms (GA). The model's parameters can be adjusted for better performance using these algorithms, which simulate real processes like swarm behaviour or genetic evolution. The dynamic adaption to new spam strategies is made easier and the number of false positives is decreased with this integration. The success of our strategy is demonstrated by our experimental analysis on a real-world email dataset. By achieving greater accuracy rates and lower false positive rates than traditional spam detection techniques, the hybrid model outperforms them. The model also shows robustness against hostile attacks and demonstrates its adaptability to various email sources and languages.
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