Autoencoder based Architecture for Mitigating phishing URL attack in the Internet of Things (IoT) using Deep Neural Networks
S.B. Gopal, C. Poongodi, D. Nanthiya, T. Kirubakaran, D. Logeshwar, B. Kulavishnu Saravanan · 2022 6th International Conference on Devices, Circuits and Systems (ICDCS) · 2022
One of the serious emerging threats on the internet is a Phishing URL attack, wherein the attackers try to steal the login credentials like username, passwords, or even bank details of the user by creating fake websites then, get into the actual website then try to corrupt or delete the information. According to a survey conducted in 2016 at least two-thirds of people were exposed to these phishing attacks so, these types of attacks must be taken seriously to avoid losses occurred by these cyber threats. There are some existing defense mechanisms like a firewall, but it is not effective in preventing different forms of attacks, since it works on a set of predefined rules, it identifies and blocks only those predefined attacks. Defending mechanism that itself able to learn the possibility of attacks so that, it not only identifies and blocks the existing forms of attacks but also any other forms of future attacks must be needed. Our proposed method uses Deep Neural Networks (DNN) model combined with autoencoders, to detect and block phishing websites in the network layer. The model is used to feature reduction and trained with the help of a dataset received from autoencoder, which consists of a set of legitimate websites and phishing websites. This architecture shows the accuracy of about 98.18% for Multiclassification Dataset. Since the memory, availability in sensor nodes is less only limited processing of data can be done security algorithms are implemented in network layers.