Efficient feature aware machine learning model for detecting fraudulent transaction in streaming environment
2022, vol.14 , no.3, pp. 3-14
The emergence of the internet and social streaming environment makes users circulate private multimedia data with similar people across global. Taking benefits from these applications; it becomes much easier for the spammer to attack digitally. As a result, an effective intrusion detection system is required. In this paper, an efficient feature extraction and selection method addressing class imbalance problems for detecting fraudulent links in a streaming environment is presented. Here an improved feature-aware machine learning-based classification algorithm for detecting fraudulent transactions in a streaming environment is presented. The results are compared over the existing supervised classification methodologies for validating the proposed methodology using standard datasets with serious class imbalance issues.
Class Imbalance, Concept Drift, Deep Learning, Ensemble Learning, Intrusion Detection System, Machine Learning, Social Network
Arati Shahapurkar, S. F. Rodd. Efficient feature aware machine learning model for detecting fraudulent transaction in streaming environment. International Journal on Information Technologies and Security, vol.14 , no.3, 2022, pp. 3-14.