SpamDL: A High Performance Deep Learning Spam Detector Using Stanford Global Vectors and Bidirectional Long Short-Term Memory Neural Networks
Jaouhar Fattahi, Mohamed Mejri, Marwa Ziadia, Ridha Ghayoula · Frontiers in artificial intelligence and applications · 2022
Spam consists of unwanted messages that are often containers of malicious code and/or links pointing to shady sites or objects that pose real dangers to a company’s machines, software, or data. Spam detection is therefore a primary security objective. Nevertheless, the detection tools available on the market are few in number and their efficiency is often limited. In this paper, we propose a spam detection tool based on deep-learning. Our tool uses bidirectional Long-Short Term Memory networks while relying on Stanford Global Vectors for word representation. We present the techniques we use. Then, we conduct a series of experiments on a family of candidate detectors. Finally, we present the performance of the selected detector.