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Improved KNN - Based 6LoWPAN Network Intrusion Detection Method

机译:基于KNN的6LOWPAN网络入侵检测方法

摘要

The present invention relates to an improved KNN-based 6LoWPAN network intrusion detection method. The present invention selects quantifiable security features which can reflect a self-security state of network elements of a 6LoWPAN network for training, and establishes a 6LoWPAN network feature space. The present invention assigns the weights to the features and transfers zero points, to alleviate the bias caused by large and small impact factors and simplify calculation; realizes construction and update of a state data table of network elements by extracting the feature data of network elements in real time, thus forming a normal contour updated according to the real-time state of the network in the feature space of the 6LoWPAN network based on the clustering effect of a KNN algorithm; and the present invention improves the KNN algorithm and redefines a basis for judging the invasion, to meet the requirements for 6LoWPAN network intrusion detection.
机译:技术领域本发明涉及一种基于KNN的6LOWPAN网络入侵检测方法。 本发明选择可测量的安全特征,其可以反映6LowPAN网络的网络元件的自我安全状态,用于训练,并建立6LowPan网络特征空间。 本发明将权重分配给特征并传输零点,以缓解大小冲击因子引起的偏差并简化计算; 通过实时提取网络元素的特征数据来实现网络元件的状态数据表的构造和更新,从而基于6LowPan网络的特征空间,根据网络的实时状态形成正常轮廓。 KNN算法的聚类效应; 并且本发明改进了KNN算法并重新定义了判断侵入的基础,以满足6LOWPAN网络入侵检测的要求。

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