Intelligent content-based cybercrime detection in online social networks using cuckoo search metaheuristic approach - Singh, Amanpreet; Kaur, Maninder (The Journal of supercomputing, 2020)
The subject of content-based cybercrime has put on substantial coverage in recent past. It is the need of the time for web-based social media providers to have the capability to distinguish oppressive substance both precisely and proficiently to secure their clients. Support vector machine (SVM) is usually acknowledged as an efficient supervised learning model for various classification problems. Nevertheless, the success of an SVM model relies upon the ideal selection of its parameters as well as the structure of the data. Thus, this research work aims to concurrently optimize the parameters and feature selection with a target to build the quality of SVM. This paper proposes a novel hybrid model that is the integration of cuckoo search and SVM, for feature selection and parameter optimization for efficiently solving the problem of content-based cybercrime detection. The proposed model is tested on four different datasets obtained from Twitter, ASKfm and FormSpring to identify bully terms using Scikit-Learn library and LIBSVM of Python. The results of the proposed model demonstrate significant improvement in the performance of classification on all the datasets in comparison to recent existing models. The success rate of the SVM classifier with the excellent recall is 0.971 via tenfold cross-validation, which demonstrates the high efficiency and effectiveness of the proposed model.