Xuanyu Huan
ABSTRACT
Abstract. Internet of Things (IoT) plays an important role in smart grids. Software-Defined Internet of Things (SD-IoT) employs the Software-Defined Networking (SDN) as the network architecture of IoT in smart grids. Hence, in smart grids, SD-IoT can achieve flexible network management using the network programmability. Nevertheless, IoT devices in smart grids face a number of security issues. The Low-rate Distributed Denial of Service (DDoS) is one of such a security issue. Detecting LDDoS is one of the most important steps in defending LDDoS. When detecting LDDoS in SD-IoT, a large number of network features are useless or even have side effects on the detection of LDDoS. Therefore, how to choose the feature subset from all features to improve the LDDoS detection performance is a key problem. In order to solve these problems, in this work, we propose ENTER, a multi-correlation information EA-based feature selection method, used to detect LDDoS for SD-IoT in smart grids. ENTER calculates the multi-correlation information of different individuals and adjusts the partial variation of the population through the multi-correlation information. ENTER is consisted of a novel multi-correlation information-based gene mutation strategy and a novel local optimum bounce strategy. The evaluation results indicate that ENTER can achieve high feature compression ratio while improve the detection performance of the LDDoS detector, in terms of detection precision, accuracy, recall, f-score and detection time.
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