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More people use the Internet to shop, access information, etc. But on the other hand, hackers implant malwares (e.g. Trojans, Worms, etc.) in the web pages to steal user information and acquire money illegally, which poses a great risk to the security of cyberspace and the privacy of users. Therefore, it is of great importance to detect malicious URLs in the field of cyberspace security. Different from most of previous methods, in this paper, we propose a method for online malicious URLs detection based on adaptive learning. By collecting the network traffic from backbone networks, we train machine learning models to detect the malicious URLs. But there is a serious problem in dynamically changing environments where the statistical properties of target variable change over time, which is known as concept drift. To address this problem, we apply a nonparametric test to correctly detect concept drifts in adaptive learning. Extensive experiments with different types of concept drifts are performed to demonstrate the feasibility of our proposed method on both artificial and real datasets. Our empirical study shows that this approach has good performance in detecting malicious URLs and concept drifts.
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