Spam Detection Using Firefly Optimization Technique

In this electronically era, communication using email has become an part of our lives. However, with the convenience of email comes the nuisance of spam messages flooding our inboxes. Detecting and filtering out these spam emails is crucial to maintain productivity and security in our online interactions. In this research project, we explore the performance of using the firefly algorithm, a nature-inspired optimization technique, for spam detection. By leveraging the unique behavior of fireflies in search and optimization, we aim to develop a robust and efficient spam detection system. We start by gathering a dataset containing a mix of spam and non-spam (ham) emails. After preprocessing the text data and extracting relevant features using techniques like Count-Vectorizer, we train a logistic regression model, a commonly used classifier for binary classification tasks. To enhance the performance of our model, we employ the Firefly algorithm to optimize its hyperactive parameters. This algorithm impersonates the flashing behavior of fireflies to iteratively search for the foremost combination of model parameters, maximizing its ability to distinguish between spam and legitimate emails. Our experimental results demonstrate promising outcomes, with the optimized model achieving high accuracy and performance measures such as precision, recall, and F1-score on a held-out test set. These findings highlight the potential of nature-inspired algorithms like the firefly algorithm to improve spam detection techniques and effectively combat email spam in real-world scenarios.