# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 1 Introduction  
Industrial control networks (ICS) use sensors, electrical devices, and controllers to monitor and regulate industrial systems. These networks can exchange data and deliver commands through communication protocols, thus enabling automated manufacturing and monitoring. However, ICS face security challenges [1], such as counterfeit attacks, unauthorized access, Denial of Service(DoS), interference, and eavesdropping. Due to the progress of ICS, conventional security measures such as identity authentication and firewalls are inadequate to deal with the ever-changing intrusion attacks [2]. In addition to firewalls, Intrusion Detection Systems (IDS) serve as a secondary defense mechanism, utilizing their adaptability to safeguard the stability and security of the industrial settings [3]. Recently, there have been initial implementations of Federated Learning (FL) in the area of intrusion detection. Federated Learning (FL) [4] is a machine learning framework that allows multiple organizations to construct machine learning models and utilize data while adhering to government regulations, user privacy protection, and data security. Federated Learning (FL) is a distributed learning approach that can effectively resolve the issue of data silos. Federated Learning (FL) has also been the subject of rudimentary applications in thefield of intrusion detection in recent years [5]. FL executes the subsequent three steps in every round: First, the server disseminates the present global model to a subset of clients. Subsequently, every client refines the overarching model by utilizing its specific training data and communicates the model modifications back to the server. Finally, the server consolidates the model updates from the clients based on a specific aggregation rule and applies the consolidated updates to update the global model. Various FL approaches employ distinct aggregation rules. However, in physical ICS systems, specific categories of abnormal incursion data are quite limited. The limited availability of data in Intrusion Detection Systems (IDS) can cause issues in accurately detecting intrusions, which have just a few instances. This can result in a bias towards the more common intrusion types and an inability to identify novel types of intrusions [6]. The reason is that Federated Learning depends on Stochastic Gradient Descent (SGD), a commonly used method for training deep networks that has demonstrated strong empirical results [7; 8; 9; 2; 10]. Sampling the training data independently and identically distributed (IID) is essential to ensure that the stochastic gradient is an unbiased estimate of the whole gradient [8; 11; 12]. However, in reality, the data sources for each client depend on how users interact with the client. Because different clients have varying functionalities and usage patterns, they often generate different types of data. As a result, this variation makes it challenging for traditional Federated Learning aggregation methods to achieve high-performance global models. Researchers have introduced various algorithms to address the challenge of sample imbalance. Recent studies [13] have demonstrated the use of contextualized embeddings and recurrent neural networks (RNNs) in fake news detection, partially alleviating the impact of sample imbalance. Progress in learning from minority samples primarily stems from improvements in both model strategies and sample utilization. Employing data generation techniques has shown great promise in enhancing a model's generalization capabilities for small-scale samples [14]. The Conditional Variational Autoencoder (CVAE), a type of generative model, consists of an encoder and a decoder. Unlike traditional discriminative models, a CVAE learns the latent distribution of the data [15; 16]. To address the aforementioned issues, this paper proposes an intrusion detection system that is predicated on Conditional Generative Federated Learning (CGFL).