ABSTRACT The primary threat to online social network (OSN) users is account compromisation. The challenge in detecting a compromised account is due to the trusted relationship established between the account owners, their friends, and the service providers. The available research which focuses on using machine learning has limitations with human experts involved in feature selection and a standardized dataset. The paper discusses users` various behaviors of users of OSN and the up-to-date approaches in detecting a compromised OSN account with emphasis on the limitations and challenges. Furthermore, we propose an enhanced machine learning approach Word Embedding and KNN (WE-KNN), which addresses the limitations faced by the previous techniques used. We detailed our proposed WE-KNN for feature extraction, selection of behavior of OSN users, and classification. Our proposed model is evaluated using the standard benchmark datasets, namely KDD Cup ‘99 and NSL-KDD and implemented it in WEKA. Besides, we used state-of-the-art evaluation metrics to assess the performance of our model. The results obtained depicts that the proposed approach in compromise account detection performs better.

中文翻译：

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使用增强的 Knn 检测受损的在线社交网络帐户

摘要 在线社交网络 (OSN) 用户面临的主要威胁是帐户泄露。检测被盗帐户的挑战在于帐户所有者、他们的朋友和服务提供商之间建立的信任关系。现有的专注于使用机器学习的研究在涉及特征选择和标准化数据集的人类专家方面存在局限性。该论文讨论了 OSN 用户的各种行为以及检测被盗 OSN 帐户的最新方法，重点是局限性和挑战。此外，我们提出了一种增强的机器学习方法 Word Embedding and KNN (WE-KNN)，它解决了以前使用的技术所面临的局限性。我们详细介绍了我们提出的用于特征提取、OSN 用户行为选择的 WE-KNN，和分类。我们提出的模型使用标准基准数据集进行评估，即 KDD Cup '99 和 NSL-KDD，并在 WEKA 中实现。此外，我们使用最先进的评估指标来评估我们模型的性能。获得的结果表明，所提出的入侵帐户检测方法性能更好。