Abstract:

The Internet of Things (IoT) is another information industry revolution after the computer, the Internet and the mobile communications. At present, IoT has been officially listed as one of the national strategic emerging industries, and its application range covers almost all areas. Secure problems such as network intrusion in the IoT art prominent increasingly. In the big data context, this paper proposes an intrusion detection model that is suitable for IoT which divides the intrusion detection procedure into three parts, which are data preprocessing, features extraction and data classification. Data normalization and data redundancy reduction are solved in the data preprocessing. The main goal of features extraction is to reduce the dimension and thus to reduce the time of data classification. Support vector machine with balanced binary decision tree algorithm that is named BDT-SVM is introduced in the data classification for training and testing the network intrusion data. Experimental results show that it can improve the accuracy of intrusion detection system by using the BDT-SVM algorithm and reduce the detection time with features extraction in the premise of ensuring accuracy.

Key words: intrusion detection, balanced binary decision tree, support vector machines, IoT security