## 2 Literature Review  
Before delving into the core of the paper, let us review the proposed strategies and approaches regarding DDoS attacks in terms of prediction, detection, or mitigation. To anticipate DDoS attacks, [PERSON] et al. [21] utilized two machine learning models: Support Vector Machine (SVM) and Random Forest (RF). Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE), two techniques for reducing the number of dimensions, are tested during the preparation of the data. The performance of models is assessed using the mean cross-validation accuracy. In the same study, they discovered that the performance of SVM is more accurate and stable than that of Logistic Regression (LR). In [22], [PERSON] et al. proposed an approach that focused on the anticipation of future attacks, with the objective of offering timely alerts to network administrators. This proactive strategy has enabled administrators to promptly implement containment measures or isolate affected hosts. The approach used by these researchers is founded upon the utilization of a Markov chain model to represent the sequence of Botnet infections. The primary aim of this model is to discern patterns of behavior that are indicative of potential attacks. The findings of the study indicated that this particular method exhibited considerable potential in generating timely alerts for detecting attacks. The accuracy rate for predicting attacks was found to be over 98%, while the maximum rate of false alarms was observed to be under 2%. [PERSON] et al. in [23] presented an innovative architectural framework that combines DDoS attack prediction with botnet identification. The architectural design was based on the principle that the sooner a system detects signs of an oncoming DDoS attack and identifies the related bots, the more efficiently it can respond to neutralize the attack. The prediction process entails recognizing early signs of a network assault before it escalates to more advanced phases. The performance evaluations employed the CTU-13 [24] and CAIDA (Center for Applied Internet Data Analysis) [25]. The evaluations effectively detected the existence of bots in the dataset, attaining an accuracy rate of 99.9%. [PERSON] et al. [26] developed a framework for classifying and predicting DDoS attacks using machine learning techniques. The framework involves selecting a dataset, choosing appropriate tools, pre-processing data, extracting features, encoding data, and dividing data into training and testing sets. The model undergoes optimization, including kernel scaling and hyper-parameter tuning, resulting in an average accuracy of 90%. Comparatively, the model's precision of defect identification improved to 85% and 79%, respectively. In [27], [PERSON] et al. introduced ShieldRNN, a novel methodology for training and prediction in Recurrent Neural Networks/Long Short-Term Memory models, to protect IoT devices from attacks. Their solution consists of an IoT node detector and a server detector. The researchers evaluated ShieldRNN on the CIC-IDS2017 dataset [28] and established benchmark outcomes for identifying DDoS attacks on the CIC-IoT2022 dataset [29]. [PERSON] et al. in [30] proposed a cost-effective method for the real-time detection of M2M traffic using the Markov chain's recurrence property. They presented a DDoS attack targeting Machine Type Communication (MTC) devices, aiming to congest fourth-generation (4G)/5G networks. The 3 rd-Generation Partnership Project (3 GPP) traffic Markov-based modeling demonstrated the impact of these attacks on mobile network elements, highlighting their detrimental effects on signaling load. The proposed detection framework can detect active intrusions in around 380 s with a 91% detection accuracy. In [31], [PERSON] et al. provided a comprehensive analysis of DDoS attacks and their impact on cyber security. They presented a hierarchical framework and analyzed studies in academic journals.