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Detection Framework for Content-Based Cybercrime in Online Social Networks Using Metaheuristic Approach

机译：基于元启发式方法的在线社交网络中基于内容的网络犯罪检测框架

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摘要

In recent years, content-based cybercrime detection has become a topic of attraction among researchers. Cybercrime hasemerged as a money-driven industry with malicious intent towards online social networks. Cyber-criminals aim to manipulatevulnerable areas in cyberspace by playing on human understanding and making a profit. They threaten minors, especiallyadolescents, who are not adequately overseen while online. To address this issue, there is an urgent need for a robustcontent-based cybercrime detection framework. The aim of this research work is to explore possible combinations of variouspreprocessing, feature selection and classification methodologies using the cuckoo search metaheuristic approach. Thisapproach seeks to improve the performance of content-based cybercrime detection system. For the purpose of this research,four publicly available datasets for cyberbullying detection have been utilized for evaluating the effectiveness of the proposedalgorithm. The algorithm was then further compared with three recent cyberbullying detection models based on variousevaluation parameters. These parameters included precision, recall and f-measure. The experimental results demonstrate theeffectiveness of the proposed approach. This approach outperformed other recent techniques on all the datasets, giving highpredictive recall value via tenfold cross-validation.
机译：近年来，基于内容的网络犯罪检测已成为研究人员的一个热门话题。网络犯罪已经成为一个以金钱为主导的行业，其恶意目的是为了建立在线社交网络。网络罪犯的目的是通过发挥人类的理解并从中获利来操纵网络空间中的脆弱区域。他们威胁未成年人，尤其是青少年，他们在网上不能得到足够的监督。为了解决这个问题，迫切需要一个强大的基于内容的网络犯罪检测框架。这项研究工作的目的是使用布谷鸟搜索元启发式方法探索各种预处理，特征选择和分类方法的可能组合。该方法旨在提高基于内容的网络犯罪检测系统的性能。出于本研究的目的，已利用四个可公开获取的用于网络欺凌检测的数据集来评估所提出算法的有效性。然后将该算法与基于各种评估参数的三个最新的网络欺凌检测模型进一步进行比较。这些参数包括精度，召回率和f测度。实验结果证明了该方法的有效性。这种方法在所有数据集上的表现均优于其他最新技术，通过十倍交叉验证可提供较高的预测召回价值。

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