Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts un...Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware installation.Thus,it is imperative to detect such threats.However,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs.In this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web attacks.Specifically,we firstly use auto-encoder to represent URLs.Then,the represented URLs will be input into a proposed composite neural network for detection.In order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.展开更多
基金This work is supported in part by the National Natural Science Foundation of China(61871140,61872100,61572153,U1636215,61572492,61672020)the National Key research and Development Plan(Grant No.2018YFB0803504)Open Fund of Beijing Key Laboratory of IOT Information Security Technology(J6V0011104).
文摘Since the web service is essential in daily lives,cyber security becomes more and more important in this digital world.Malicious Uniform Resource Locator(URL)is a common and serious threat to cybersecurity.It hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware installation.Thus,it is imperative to detect such threats.However,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs.In this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web attacks.Specifically,we firstly use auto-encoder to represent URLs.Then,the represented URLs will be input into a proposed composite neural network for detection.In order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.
