# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 2 Related Work  
Anomaly detection methods based on DL models have demonstrated outstanding performance because of their ability to capture complex patterns in network traffic [22]. The DL models used in anomaly detection include convolutional neural networks (CNNs), DNNs, Gaussian mixture models (GMMs), generative adversarial networks (GANs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and deep autoencoders (AEs). Among the anomaly detection methods, rule detection approaches train models using historical attack data to extract features that represent these past attacks, known as fingerprint features. Consequently, the historical attacks and their fingerprint features are covered within the rule detection approach [25]. Attacks are classified by comparing the analyzed data with the fingerprint features of the attacks covered in rule-based detection methods. Rule detection methods based on DL models have been widely studied, with a focus on protocol and application attacks in UAV networks [23; 27; 28] and IoUAVs [29; 30; 31]. Various DL models, such as CNNs [27], DNNs [29], and RNNs [31], have been used to extract static or temporal fingerprint features. Hybrid models that combine CNNs and LSTM networks have also been employed to capture data anomaly features [28; 29], making them suitable for 6G protocol traffic, which is inherently a time series [32]. These methods have been validated with datasets such as CICDDoS2019 [23], INS [27], KDD-CUP99 [28], CICIDS [29], MDVD [30], and 5G-NIDD [31]. However, existing rule detection methods are ineffective for classifying uncovered protocol attacks in 6G NTNs, as prior knowledge of the attack is not available. In addition, existing methods cannot be used to identify anomalous data. On the other hand, baseline detection methods can be used to monitor uncovered protocol attacks by detecting deviations from normal protocol classifications with DL models, which can be trained on the attack-free protocol traffic data of 6G NTNs. Baseline detection methods have been studied for various scenarios, including UAV networks [24; 33; 34] and IoUAVs [35]. DL models and techniques such as YOLOv8 [24], one-class support vector machines (OC-SVMs) [33], LSTM networks [34], and Bayesian-LSTM [35] have been employed to establish normal baseline behaviors. However, baseline detection methods, which output Boolean results indicating only the presence or absence of attacks, have difficulty classifying uncovered protocol attacks and identifying anomalous protocol sequences in 6G NTNs. To combine detection and classification, rule and baseline detection methods have been sequentially integrated to monitor attacks in UAV networks, IoUAVs, and the IoT in recent research. For IoUAVs, an AE and a DNN model [36] have been combined. A toolset is used to define normal behaviors and cover attack conditions, whereas the DNN model is used to classify normal behaviors, cover attacks, and uncover attacks. For UAV networks [37] and IoT [38] attacks, data are first processed through rule detection methods using RNN-LSTM [37] or CNN-LSTM [38] to identify covered attacks. Data that do not match covered attacks are then analyzed via baseline detection methods using the synthetic minority oversampling technique (SMOTE) algorithm [37] or convolutional LSTM [38] to detect uncovered attacks. These hybrid approaches achieve over 90% accuracy on the CICIDS2017, NSL-KDD, and KDD Cup-99 datasets. However, existing rule and baseline detection methods are merely sequentially connected; thus, these approaches are limited to the capabilities of the two individual methods, including detecting uncovered and covered attacks and classifying covered protocol attacks. With respect to detection, classification, and identification requirements, these rule detection methods, baseline detection methods, and sequential methods fail in terms of two key aspects. First, these methods fail in classifying uncovered attacks without establishing new classifications.