# Intrusion Detection Model Based on Improved Transformer
## 1 Introduction
The CNN was used for convolution to capture local features, and the RNN was used to capture temporal features. This model achieved good accuracy in both binary and multi-class classification tasks. In reference [14], a BiLSTM-based intrusion detection model was proposed, which outperformed traditional LSTM and other state-of-the-art models in terms of accuracy, precision, recall, and \(F1\)-_score_. In reference [15], a technique for oversampling based on GANs and feature selection was proposed. This technique first modeled a complex high-dimensional attack distribution using Gradient Penalty Wasserstein GAN to generate additional attack samples. Then, a subset of features representing the entire dataset was selected using variance analysis. Finally, a rebalanced low-dimensional dataset was generated for machine learning training. In summary, deep learning algorithms have made significant breakthroughs in the field of intrusion detection and have become an important technology. These algorithms can automatically learn features from input data and are used to detect potential network intrusion behaviors. Although intrusion detection systems have made significant progress with the development of deep learning, they still face three challenges: long training times for models, imbalanced datasets, and poor performance in multi-class classification. Therefore, this paper proposes an enhanced Transformer-based intrusion detection model that offers the following contributions:  
1. This paper proposes a data processing strategy to address the challenges of high-dimensional features and class imbalance in intrusion detection datasets. Specifically, two techniques are proposed: (i) A stacked auto-encoder is used to reduce the dimensionality of the dataset by encoding the data features based on their original distribution. This not only accelerates model training but also preserves the information content of the data features. (ii) A new under-sampling method based on the K-nearest neighbors (KNN) algorithm is proposed, which under-samples normal samples and over-samples abnormal samples using Borderline-SMOTE. This hybrid sampling approach balances the dataset while mitigating class overlap issues, thereby improving the detection performance of the model. 2. To enhance the classification performance of the model, this paper proposes an improved position encoding method for the Transformer. By incorporating positional information from the features in the intrusion detection dataset, the model can capture dependencies among the features, thereby enhancing its detection capability. 3. To enhance the model's capability to handle multiple classes, this paper proposes a two-stage learning strategy. This strategy involves an initial coarse binary prediction, followed by inputting this prediction, along with the original features, into a multi-classification model. This results in more accurate multi-classification predictions. This chapter provides an overview of intrusion detection, including its background and current research status using machine learning and deep learning techniques. It also highlights three key challenges in intrusion detection and describes how this paper addresses these issues. The subsequent chapters are structured as follows:  
1. Section 2, Related Work, presents the current research status of intrusion detection in addressing the aforementioned challenges and discusses the limitations of existing research. It also examines the use of a Transformer in intrusion detection and identifies its shortcomings. Finally, it introduces the proposed improvements in this paper to address these limitations. 2. Section 3, Materials and Methods, presents the proposed model and its various modules and improvements. 3. Section 4, Results, presents the experimental results and discusses their implications in light of the research contributions. 4. Section 5, Discussion, provides an in-depth analysis of the proposed model in this paper, including its strengths, limitations, and practical implications.