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Input data preprocessing for bots detection using the dendritic cells algorithm

机译:使用树突状细胞算法进行机器人检测的输入数据预处理

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The dendritic cells algorithm (DCA) is an immune-inspired intelligent algorithm and is based on an abstract model of the behavior of dendritic cells (DCs) in immunology. Meanwhile, a number of bots based on diverse protocols, including P2P, IRC, DNS and HTTP, have become an increasing threat to network security. So many researchers use the DCA to detect a single bot in computer system. But the DCA's input is the time series data consisting of antigens and signals. The complex raw data must be preprocessed to form the input stream of antigens and signals. The data preprocessing of the DCA includes raw data collection phase, selection, extraction and mapping phase of antigens and signals. This paper proposed the several methods of input data preprocessing for bots detection using the DCA. These methods are suit for the different bots detection, such as IRC and P2P bots detection.
机译:树突状细胞算法(DCA)是一种受免疫启发的智能算法,基于免疫学中树突状细胞(DC)行为的抽象模型。同时,许多基于多种协议的漫游器,包括P2P,IRC,DNS和HTTP,已对网络安全性构成越来越大的威胁。因此,许多研究人员使用DCA来检测计算机系统中的单个bot。但是DCA的输入是由抗原和信号组成的时间序列数据。必须对复杂的原始数据进行预处理,以形成抗原和信号的输入流。 DCA的数据预处理包括原始数据收集阶段,抗原和信号的选择,提取和定位阶段。本文提出了几种使用DCA进行机器人检测的输入数据预处理方法。这些方法适用于不同的僵尸程序检测,例如IRC和P2P僵尸程序检测。

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