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.

中文翻译：

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使用布谷鸟搜索元启发式方法在在线社交网络中进行基于智能内容的网络犯罪检测

最近，基于内容的网络犯罪这一主题得到了大量报道。基于网络的社交媒体提供商需要有能力准确和熟练地区分压迫性物质以保护他们的客户。支持向量机（SVM）通常被认为是针对各种分类问题的有效监督学习模型。然而，SVM 模型的成功依赖于其参数的理想选择以及数据的结构。因此，这项研究工作旨在同时优化参数和特征选择，以建立 SVM 的质量。本文提出了一种新的混合模型，即布谷鸟搜索和 SVM 的集成，用于特征选择和参数优化，以有效解决基于内容的网络犯罪检测问题。所提出的模型在从 Twitter、ASKfm 和 FormSpring 获得的四个不同数据集上进行了测试，以使用 Python 的 Scikit-Learn 库和 LIBSVM 识别欺凌术语。与最近的现有模型相比，所提出模型的结果表明，所有数据集的分类性能都有显着提高。具有优秀召回率的SVM分类器通过十倍交叉验证的成功率为0.971，证明了所提出模型的高效性和有效性。与最近的现有模型相比，所提出模型的结果表明，所有数据集的分类性能都有显着提高。具有优秀召回率的SVM分类器通过十倍交叉验证的成功率为0.971，证明了所提出模型的高效性和有效性。与最近的现有模型相比，所提出模型的结果表明，所有数据集的分类性能都有显着提高。具有优秀召回率的SVM分类器通过十倍交叉验证的成功率为0.971，证明了所提出模型的高效性和有效性。