MF-Adaboost: LDoS attack detection based on multi-features and improved Adaboost

Abstract A low-rate denial of service (LDoS) attack is a precise network attack that aims at reducing the quality of the network service. Many networks do not have an effective mechanism for defending against LDoS attacks, including the emerging Internet of Things. In this paper, we propose an LDoS attack detection method that is based on multiple features of network traffic and an improved Adaboost algorithm (MF-Adaboost). Based on an analysis of the network traffic, we construct a network feature set that is used for feature calculation and feature selection of network traffic data. Feature calculation can extract the most useful information from the network traffic data and reduce the scale of the network data. Feature selection is used to select the optimal classification features to ensure that the detection algorithm can be effectively trained. This method utilizes the Adaboost algorithm, which is a classification algorithm in the field of machine learning. The well-trained Adaboost algorithm can effectively identify LDoS attack traffic. We improve the Adaboost algorithm to alleviate the imbalance of the sample weights. Experiments are conducted on the NS2 simulation platform and a test-bed platform to evaluate the performance of our method. The experimental results demonstrate that our method can detect LDoS attacks effectively.

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