Using Adversarial Examples to Bypass Deep Learning Based URL Detection System

Due to the outstanding performance on the feature extraction and classification, Deep Learning (DL) models have been developed in many existing network systems. Nowadays, the DL can be used for cyber security systems such as building the detection system for the malicious Uniform Resource Locator (URL) links. For the practical usage, the DL-based models are proved to have better accuracy and efficiency on detecting malicious URL links in the current networking systems. However, some DL models are vulnerable to the subtle change of inputs such as the Adversarial Example (AE) which also exists in the URL detection scenario: the malicious URL links can bypass the DL-based detection with a crafty change to threat the security of the network systems. In this paper, we present an AE generation method against DL-Based web Uniform Resource Locator (URL) detection system by generating AEs. We could generate AEs with minimum changes (one byte in the URL) in the inputs to bypass the DL-based URL classification model with a high success rate.