Countering malicious URLs in Internet-of-Thing (IoT) using a knowledge-based approach and simulated expert

This study proposes a novel methodology to detect malicious URLs using simulated expert (SE) and knowledge base system (KBS). The proposed study not only efficiently detects known malicious URLs, but also adapt countermeasure against the newly generated malicious URLs. Moreover, this study also explored which lexical features are more contributing in final decision using a factor analysis method and thus helped in avoiding involvement of human expert. Further, we applied the following state-of-the-art ML algorithms, i.e., Naïve Bayes (NB), Decision Tree (DT), Gradient Boosted Trees (GBT), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), and Random rest (RF), and evaluated the performance of these algorithms on a large-scale real data set of data-driven Web application. The experimental results clearly demonstrated the efficiency of NB in the proposed model as NB outperformed when compared to the rest of aforementioned algorithms in term of average minimum execution time (i.e., 3 seconds) and was able to accurately classify the 107586 URLs with 0.2% error rate and 99.8% accuracy rate