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首页> 外文期刊>Enterprise information systems >Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection
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Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

机译：基于遗传算法的特征选择对在线社交网络中的垃圾邮件发送者和非申报器内容的分类

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摘要

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.
机译：在线社交网络的出现调用社交行为者以数字方式分享他们的个人信息。此外，它提供了与全球相同兴趣的人保持联系的设施。利用这些服务;它已成为一个迷人的试验台，以邀请像垃圾邮件发送者这样的各种威胁。 OSN中的垃圾邮件发送者的检测是最关键的任务之一。垃圾邮件发送者不仅传播不需要的广告或糟糕的广告，而且在其他人的档案中进行了某些恶意活动。通过清楚地了解不同威胁的活动，通过使用社交网络服务检测这些活动所涉及的垃圾邮件发送者内容和配置文件所需的一些增量和准确的方法。因此，本文的重点是检测垃圾邮件发送者内容和帐户，特别是在名为Twitter的领先微博平台上。我们提出了一种混合方法，它利用各种机器学习算法的能力来分离垃圾邮件发送者和非本网者内容和帐户。最初，称为遗传算法的优化算法分析了各种特征，并选择影响用户帐户行为的最佳合适功能，然后使用这些功能来训练分类器。我们的框架以有效的方式实现了各种各样的垃圾邮件发送者和非本国内容。最后，为了证明我们提出的框架的效率，通过一些现有的最先进技术进行了比较分析。实验分析表明，我们的方法达到了99.6％的高检测率，比其他最先进的技术更好。

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