Verbesserte Klassifizierungstechnik für Spam-Filterung

Communication is a fundamental human need. SMS and email messages are a method that is acceptable, efficient, and cost-effective. Nowadays, most official and personal communications are conducted via email, but these emails are not very secure due to untrustworthy networks and intermediary network attackers. In our work, we examine the traditionally available techniques for spam filtering. Additionally, a new technique is introduced that employs a hybrid methodology incorporating the Bayesian classifier and a neural network. Furthermore, the performance of the proposed technique is compared to that of the traditional Bayesian classifier. The results show that the performance of the proposed classification technique is significantly better than that of the traditional technique. Thus, the proposed technique is highly accurate and more efficient than the traditionally available classification techniques.