E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks
Source abstract
Abstract
Spam has turned into a big predicament these days, due to the increase in the number of spam emails, as the recipient regularly receives piles of emails. Not only is spam wasting users’ time and bandwidth. In addition, it limits the storage space of the email box as well as the disk space. Thus, spam detection is a challenge for individuals and organizations alike. To advance spam email detection, this work proposes a new spam detection approach, using the grasshopper optimization algorithm (GOA) in training a multilayer perceptron (MLP) classifier for categorizing emails as ham and spam. Hence, MLP and GOA produce an artificial neural network (ANN) model, referred to (GOAMLP). Two corpora are applied Spam Base and UK-2011 Web spam for this approach. Finally, the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Spam and Phishing Detection · Network Security and Intrusion Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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