Decision Support System of Juvenile Hacking Classification

• In Event: Poster Session 1

Abstract

Juvenile hacking has become a major concern. This research tends to develop a more precise decision support system of juvenile hacking classification via the use of a stacking ensemble learning (SEL) that combines 20 machine learning (ML) models using a meta-learner. The base models are trained based on a complete training set of teenager hacking data, then the meta-model is trained on the outputs of the base models to make a final classification of hacking behavior. This method harnesses the predictive power of a series of models, thereby enhancing the generalization capability and mitigating the risk of selection bias associated with individual algorithms. The proposed SEL model ensures the flexibility and achieves greater accuracy than the generic ML models, which is more transparent than the black box model inherent in the realm of ML. The factors triggering hacking behavior are ranked via the SEL model and then the selected features get to decide the maximum depth of the decision tree (DT). The pathway from data processing, feature engineering, SEL to DT interpretation model elucidates the relationship and decisive mechanism among the risk factors. The research framework is able to interpret the decision chain of hacking behavior through teenagers’ risk factors.

Author

• Siying Guo, Wayne State University