Malware Detection System Using Natural Language Processing Based on Website Links

Various approaches exist when building a detection model to capture Cyber-Threats but most of these approaches employ a post-active methodology-trying to detect the threats after they have occurred. We aimed to develop a model that would employ a pro-active approach by understanding the semantic and linguistic nature of their source of origin-urls and from there building a classifier that can identify potential threats. Our Decision Tree classifier achieved an accuracy of 95% on the test set showing its potential to detect cyber-threats in real life scenarios. And since our model uses a classical algorithm as opposed to deep learning methods, our model would be computationally less expensive and lightweight making it easy to deploy in real world web applications.