### Related Work
The authors of [35] proposed a three-layer design using machine learning approaches for tasks related to preprocessing, binary classification, and multi-class classification. [PERSON] summarized the current developments in deep learning techniques for identifying unknown attacks, highlighting several strategies that have exhibited exceptional performance [36]. [PERSON] presented a revolutionary approach that combines deep hierarchical neural networks with machine learning, achieving an astounding accuracy of 99.07% [37]. Furthermore, the authors of [38] reported a detection technique based on a convolutional neural network and meta-learner, utilizing a comprehensive dataset created by merging five distinct datasets. The experimental findings demonstrate the method's adaptability. [PERSON] suggested a novel method that enhances the accuracy for all types of attacks on the NSL-KDD dataset by training a fuzzy c-mean eigen analysis model at decision boundary points [39]. Moreover, [PERSON] introduced an unsupervised domain adaptation technique and a hierarchical attention triple network, both of which accurately identify previously unknown attacks, as confirmed by experimental findings [40]. [PERSON] proposed a novel method that combines an autoencoder and a variational autoencoder to efficiently detect unknown threats based on stream characteristics. This method outperforms one-class Support Vector Machines and standard autoencoders, making it valuable in identifying unknown attacks [41]. The author examined two deep generation techniques in their investigation: an adversarial autoencoder with conditional denoising and an autoencoder combined with the k-nearest neighbor algorithm. Their aim was to develop an intrusion detection system with robust detection capabilities for unknown threats. To assess the performance of these three approaches, the authors conducted experiments on four datasets. The results clearly demonstrated the potential of the proposed approach in enhancing the resilience of intrusion detection systems [42]. Furthermore, [PERSON] presented an approach that combines autoencoders by first selecting the best subset of features using feature selection. The subsequent step involves integrating multiple autoencoders to identify unknown threats. The experimental findings showcase the method's robustness and effectiveness in detecting unknown attacks [43]. Some effective solutions have appeared for the better detection of unknown attacks [44; 45; 46; 47; 48; 49; 50; 51; 52]. These methods use correlated autoencoders, variational autoencoders, and conditional variational autoencoders and combine them with different methods. The experiments have yielded good results. To address the challenges, we developed a unique intrusion detection system (IDS) called the LVAE. The LVAE effectively identifies unknown types of attacks by integrating a logarithmic hyperbolic cosine (log-cosh) reconstruction loss function. This function optimizes the potential space between the input data and reconstructed data, surpassing conventional variational autoencoders (VAEs). Consequently, the LVAE significantly enhances the quality of generated unknown attacks. We employed eight methods to extract the features and employed multiple techniques to identify unknown attacks before selecting the most accurate approach. Through extensive testing and comparison, we demonstrate that the LVAE reliably detects unknown types of attacks, ensuring robust and efficient network security.