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首页> 外文期刊>Soft computing: A fusion of foundations, methodologies and applications >MTD-Spamguard: a moving target defense-based spammer detection system in social network
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MTD-Spamguard: a moving target defense-based spammer detection system in social network

机译：MTD-SPAMGUARD：社交网络中的移动目标防御垃圾邮件发送系统

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摘要

Machine learning classifiers are currently the state of the art for spammer detection tasks in SNSs. Note, however, that these classifiers fail to detect adaptive spammers that dynamically change their spamming strategies or behaviors and attempt to pose as legitimate users. In this paper, we propose an efficient spammer detection system (which we call MTD-Spamguard) wherein the notion of MTD is applied to increase the robustness of well-known machine learning classifiers against the adaptive spammers in SNSs. The system introduces a new method of MTD wherein the concept of differential immunity of different classifiers is employed to detect the spammers. To classify a single user in the test dataset, we pick one of the appropriate trained classifiers from multiple classifiers and then use its classification output. To choose the appropriate classifier, we design an effective classifier switching strategy by formulating the interaction of users (normal users and spammers) and detector (which hosts the machine learning classifier) as a repeated Bayesian Stackelberg game. The classifier switching strategy provides strong Stackelberg equilibrium between users and detector, maximizing the accuracy of classification and reducing the misclassification of spammers. The system achieves 30% gain in classification accuracy over the Facebook dataset (constructed in our recent work).
机译：机器学习分类器目前是SNSS中的垃圾邮件发送者检测任务的最新技术。但请注意，这些分类器无法检测自适应垃圾邮件发送者，可动态改变其垃圾邮件策略或行为，并尝试构成合法用户。在本文中，我们提出了一种有效的垃圾邮件发送系统（我们呼叫MTD-SPAMGUARD），其中应用MTD的概念来增加众所周知的机器学习分类器对SNSS中的自适应垃圾邮件器的鲁棒性。该系统介绍了一种新的MTD方法，其中采用不同分类器的差分免疫的概念来检测垃圾邮件发送者。要在测试数据集中对单个用户进行分类，我们从多个分类器中选择一个适当的训练分类器，然后使用其分类输出。为了选择适当的分类器，我们通过将用户（普通用户和垃圾邮件发送者）的交互和检测器（托管机器学习分类器）作为重复的贝叶斯贝尔伯格游戏进行设计，设计有效的分类器切换策略。分类器切换策略在用户和检测器之间提供强大的Stackelberg平衡，最大化分类的准确性并降低垃圾邮件发送者的错误分类。系统在Facebook DataSet上实现了30％的分类准确性（在我们最近的工作中构建）。

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