A Secure Deep Autoencoder-based 6G Channel Estimation to Detect/Mitigate Adversarial Attacks

Abstract

systems to satisfy the development of adversarial attacks (AA) detection and mitigation for 6G wireless communication networks. It is critically required to confront the threats efficiently, abandoning the current incapable CE-targeted security systems that cannot adapt to the upgradable attacks [8], [9]. In risk-sensitive systems safety, detecting AA is a challenging issue as enormous traffic of suspicious activities is discovered every day. The impact of these complex attacks is increasing, introducing additional complications to the current attacks. Moreover, cybersecurity has become a prioritized essential topic in the modern scientific community. Therefore, monitoring and analyzing network traffic is essential to detect potential AA. The main risk in traditional and machine learning (ML)-based security systems is the insufficiency of distinguishing AA in 6G communication networks as AA manipulates signals or data in ways that are not detectable by traditional measures [10]. To this end, it is important to design 6G networks with security in mind and to implement best practices for securing the network against AA. This may involve developing new security measures that are specifically designed to detect and defend against AA. Deep Autoencoders (DAEs) are a type of NN that can be trained to learn a compressed representation of the input data, also known as the encoding, and then use this encoding to reconstruct the original input, also known as the decoding [11]- [19]. By training an AE on a set of received signals and their corresponding channel characteristics, the AE can learn to extract features that are relevant to CE. The encoding produced by the AE can then be used as a representation of the received signal, which can be fed into a CE to estimate the channel characteristics. This can be particularly useful in scenarios where the channel is highly complex or timevarying, as the AE can adapt to changes in the channel over time and provide more accurate estimates of the channel characteristics.Hence, this work proposes a secure deep autoencoder (DAE)based communication environment (CE) model to address the challenges of accurately detecting and preventing adversarial attacks (AA) in 6G wireless communication networks with minimal complexity. To the best of the authors' knowledge, this outperforming integrated DAE model with its performance has not been achieved previously. It presents a valuable contribution to the field by introducing:• A sufficient DAE-based CE 6G model with a secure transmission protocol that uses transmitted signal parameters to learn and detect AA. The model provides a feasible solution for deep learning training data requirements, avoiding overfitting.