首页|Reports from Tsinghua University Highlight Recent Findings in Machine Learning ( Network Anomaly Detection Via Similarity-aware Ensemble Learning With Adsim)
Reports from Tsinghua University Highlight Recent Findings in Machine Learning ( Network Anomaly Detection Via Similarity-aware Ensemble Learning With Adsim)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating in Beijing, People's R epublic of China, by NewsRx journalists, research stated, "The last decade has s een the increasing application of machine learning to various tasks, including n etwork anomaly detection. But anomaly detection methods based on a single machin e learning algorithm usually fail to achieve good results, since network traffic have complex and changeable patterns." Financial supporters for this research include National Key R&D Pro gram of China, Tsinghua University-China Telecom Joint Research Institute for Ne xt Generation Internet Technology. The news reporters obtained a quote from the research from Tsinghua University, "Therefore, many solutions based on ensemble learning have been proposed to addr ess this problem. However, most previous studies have the main drawback that the y overlook the similarity between the weak classifiers, which may degrade the de tection performance. What is more, most existing works use offline and supervise d algorithms, which means a large number of computing resources and reliable lab els are necessary during the training period. In this paper, we propose ADSim , an online, unsupervised, and similarity -aware network anomaly detection algorit hm based on ensemble learning. For a similarity -aware scheme, the target of ADS im can be intuitively described as recognizing the similar weak classifiers duri ng the training phase and treat them as a whole. To achieve this, ADSim first in crementally maintains a distance matrix to record the similarity between the cla ssifiers in the training phase and uses Hierarchy Clustering to group the simila r classifiers. In the detecting phase, each cluster will be assigned a weight de pending on the consistency of the detection results of the classifiers within it . Moreover, the working procedure of ADSim is online and unsupervised, which sig nificantly improves its practicality. We test ADSim on two datasets, MAWILab and CIC-IDS-2017."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningTsinghua University
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