Cuckoo inspired stacking ensemble framework for content‐based cybercrime detection in online social networks
Amanpreet Singh, Maninder Kaur · Transactions on Emerging Telecommunications Technologies · 2020
Abstract In recent times, the topic of content‐based cybercrime has gained significant attention. It is the requirement of today's era for social media companies to meet the challenges faced in differentiating the oppressive content in both a precise and proficient manner, thereby securing their clients. This article proposes a novel cuckoo inspired stacking ensemble framework that is the integration of Cuckoo Search and several machine learning models. The proposed framework automatically seeks for near‐optimal combinations of classification techniques along with their tuning parameters for efficiently solving the problem of content‐based cybercrime detection. Four datasets obtained from Formspring, ASKfm, and Twitter are used for testing purposes. The experimental results showcased significant improvement in the performance of classification on all the datasets in comparison to state‐of‐art classification models. The success rate of the proposed model with the excellent recall is 0.984 via 10‐fold cross‐validation demonstrates its high efficiency and effectiveness.