Autoencoder-based architecture for identification and mitigating phishing URL attack in IoT using DNN
By: Gopal, S. B
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Contributor(s): Poongodi, C.
Publisher: USA Springer 2023Edition: Vol.104(4), Dec.Description: 1227-1240p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: As a new and potentially devastating form of cyberattack, ‘Phishing’ URLs pose a risk to users by impersonating legitimate websites in an effort to obtain sensitive information such as usernames, passwords, and financial details. At least two-thirds of persons were hit by these phishing assaults in 2016, according to a survey (Gopal et al. in IOP Conference Series: Materials Science and Engineering, vol. 1055, no. 1, p. 012072. IOP Publishing ). Therefore, these attacks must be addressed carefully to prevent financial losses. In order to detect and prevent new kinds of assault, in addition to the ones already in use, a defense mechanism that can learn the possibilities of attacks is required. In the IoT (Internet of Things), everything is managed remotely via a web address (URL). So, intruders can simply gain access to the controls by stealing the URL used to access them from a distant location. To identify and prevent access to phishing sites at the network layer, we offer an approach that combines a deep neural networks (DNN) model with autoencoders. In order to train the model for feature reduction, an autoencoder sends it a dataset that includes both legitimate and phishing domains to analyze. The effectiveness of the suggested approach has been tested on a number of datasets, including Open phish, UCI, Mendeley, and PhishTank. For the multiclassification dataset, these metrics for this design are: 92.89% accuracy, 93.07% recall, 92.75% precision, and 92.21% F1-score.
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0297 |
As a new and potentially devastating form of cyberattack, ‘Phishing’ URLs pose a risk to users by impersonating legitimate websites in an effort to obtain sensitive information such as usernames, passwords, and financial details. At least two-thirds of persons were hit by these phishing assaults in 2016, according to a survey (Gopal et al. in IOP Conference Series: Materials Science and Engineering, vol. 1055, no. 1, p. 012072. IOP Publishing ). Therefore, these attacks must be addressed carefully to prevent financial losses. In order to detect and prevent new kinds of assault, in addition to the ones already in use, a defense mechanism that can learn the possibilities of attacks is required. In the IoT (Internet of Things), everything is managed remotely via a web address (URL). So, intruders can simply gain access to the controls by stealing the URL used to access them from a distant location. To identify and prevent access to phishing sites at the network layer, we offer an approach that combines a deep neural networks (DNN) model with autoencoders. In order to train the model for feature reduction, an autoencoder sends it a dataset that includes both legitimate and phishing domains to analyze. The effectiveness of the suggested approach has been tested on a number of datasets, including Open phish, UCI, Mendeley, and PhishTank. For the multiclassification dataset, these metrics for this design are: 92.89% accuracy, 93.07% recall, 92.75% precision, and 92.21% F1-score.
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