Network anomaly detection via similarity-aware ensemble learning with ADSim

The last decade has seen the increasing application of machine learning to various tasks, including network anomaly detection. But anomaly detection methods based on a single machine learning algorithm usually fail to achieve good results, since network traffic have complex and changeable patterns. Therefore, many solutions based on ensemble learning have been proposed to address this problem. However, most previous studies have the main drawback that they overlook the similarity between the weak classifiers, which may degrade the detection performance. What’s more, most existing works use offline and supervised algorithms, which means a large number of computing resources and reliable labels are necessary during the training period.In this paper, we propose ADSim, an online, unsupervised, and similarity-aware network anomaly detection algorithm based on ensemble learning. For a similarity-aware scheme, the target of ADSim can be intuitively described as recognizing the similar weak classifiers during the training phase and treat them as a whole. To achieve this, ADSim first incrementally maintains a distance matrix to record the similarity between the classifiers in the training phase and uses Hierarchy Clustering to group the similar classifiers. In the detecting phase, each cluster will be assigned a weight depending on the consistency of the detection results of the classifiers within it. Moreover, the working procedure of ADSim is online and unsupervised, which significantly improves its practicality. We test ADSim on two datasets, MAWILab and CIC-IDS-2017. The results show that ADSim outperforms the state-of-the-art ensemble learning methods and has ideal runtime performance.