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Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain

机译：通过将XSS扫描仪集成到FOG节点中，增强了大数据安全性以进行中小企业增益

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Advancement in embedded Nano-technologies empowers IoT technology which serves as the backbone technology for many small and medium enterprises. Evolution of smart devices proved as a supreme source of data, denoted as Big data, that is analyzed to instantly extricate constructive and valuable information that helps the organization in many ways. As it may embrace sensitive information, hence, it turned out to be the fascinating target of multitude of attacks that aims at stealing the information, resulting into privacy breaches. XSS attack is one such security attack that supports the attacker to intrude into user's personal space. Therefore, this paper is focused on designing an approach that detects XSS attack in IoT network to protect the data privacy. It employs Convolution Neural Network (CNN) to detect the XSS attack payload after applying certain data preparation methods. This approach helps in preventing privacy breaches which eventually helps the enterprises in strengthening their bond with their users. The experimental results unveiled that the approach achieves a detection accuracy of 99% after the successful execution of data preparation methods.
机译：嵌入式纳米技术的进步Empowers IoT技术，作为许多中小企业的骨干技术。被证明的智能设备的演变被证明为最高数据来源，表示为大数据，分析以立即提取有助于这些组织的建设性和有价值的信息。因此，它可能拥抱敏感信息，因此，事实证明是旨在窃取信息的众多攻击的迷人目标，从而导致隐私违规行为。 XSS攻击是一个这样的安全攻击，支持攻击者侵入用户的个人空间。因此，本文的重点是设计一种检测IOT网络中XSS攻击以保护数据隐私的方法。它采用卷积神经网络（CNN）来检测应用某些数据准备方法后检测XSS攻击有效载荷。这种方法有助于防止隐私违规行为，最终帮助企业加强与用户的联系。实验结果推出了该方法在成功执行数据准备方法后，该方法实现了99％的检测精度。

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