Chongqing University of Posts and Telecommunications

Yuan Zhuang

Ping Wang

Min Wei

Tao Yang

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.

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Chongqing, China

Yuan Zhuang