Continuous Learning and Multimodal Techniques for Next-Generation Spam Detection Systems

The problem of email spam is still extremely topical since the unsolicited bulk messages change in both content and method. This essay examines machine learning-based spam filters on a test email dataset that consists of spam and valid (ham) emails. We use four classifiers, namely Naive Bayes, Support Vector Machine (SVM), Random Forest and Logistic Regression and compare them in terms of measures of accuracy, precision, recall, and F1-score. The models are all high in spam detection accuracy (greater than 90 percent), with SVM being a little better in the overall accuracy (approximately 95.5 percent) and accuracy. Naive Bayes has the best spam recall (around 94.2) but with a more significant number of false positives and a trade-off between spam captures and legitimate email loss. We use extensive text preprocessing (tokenization, stop-word elimination, stemming) and make comparisons of feature representations: word embeddings vs. term frequency-inverse document frequency (TF-IDF). TF-IDF features performed better on this dataset than the average word embeddings on this dataset. In the fight against constantly evolving spam techniques, we consider the use of continuous learning approaches that dynamically update the filter to overcome concept drift, and we consider multimodal approaches that use textual and image data or link analysis to detect material that the purely textual filter fails to detect. As we have found, it is indeed true that using fixed machine learning models is effective in detecting spam and doing it with a high level of accuracy, however next-generation systems do need to constantly learn and make use of several data modalities to serve as a resource to robust and adaptive spam filtering in dynamic real-life scenarios.