Yu, L.; Xu, L.; Jiang, X. An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder. Appl. Sci.2023, 13, 12492.
Yu, L.; Xu, L.; Jiang, X. An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder. Appl. Sci. 2023, 13, 12492.
Yu, L.; Xu, L.; Jiang, X. An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder. Appl. Sci.2023, 13, 12492.
Yu, L.; Xu, L.; Jiang, X. An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder. Appl. Sci. 2023, 13, 12492.
Abstract
The rising number of unknown-type attacks on the Internet emphasizes the significance of developing efficient intrusion detection systems, even if machine learning-based techniques can detect unknown types of attacks. The necessity for innovative techniques is highlighted by the possibility that traditional machine learning techniques will not be sufficient for identifying these unknown types of attacks. In this research, we address this difficulty by proposing a deep learning-based solution: the log-cosh variational autoencoder (LVAE). When it comes to understanding intricate data distributions and creating freshly reconstructed data, LVAE inherits the strong modeling skills of the variational autoencoder (VAE). To better imitate discrete features of actual attacks and generate unknown types of attacks, this study develops an effective reconstruction loss term employing the logarithmic hyperbolic cosine (log-cosh) function in the log-cosh variational autoencoder (LVAE). When compared to conventional VAEs, LVAE exhibits promising potential for effectively generating data that closely resembles an unknown attack, a crucial capability for increasing the unknown attack detection rate. To categorize the generated unknown-type data, eight feature extraction and classification techniques were used. Using the most recent CICIDS2017 dataset, numerous experiments were carried out, training under varying amounts of real and unknown-type attacks. Our optimal experimental results outperformed several state-of-the-art techniques with accuracy and average F1 scores of 99.89% and 99.83%, respectively. Outstanding results were also shown by the suggested LVAE strategy for producing unknown attack data. In general, our work sets a strong basis for the accurate and efficient identification of unknown types of attacks and contributes to the development of intrusion detection techniques.
Keywords
intrusion detection; variational autoencoder; deep learning attack of unknown type
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
