# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 3 Literature Studies on SCADA Security  
In the literature, studies carried out on the detection of DDoS attacks against SCADA systems are considerably popular. These studies have frequently used machine learning- and deep learning-based methods for attack detection. Some of these works are summarized below. [PERSON] et al. performed a study to detect cyberattacks on SCADA systems. The authors created a dataset using a test environment. They employed random forest, decision tree, logistic regression, Naive Bayes, and KNN algorithms in their study for attack detection [17]. [PERSON] and colleagues worked on potential cyberattacks at Mississippi State University's SCADA Security Lab and investigated the security vulnerabilities of the most widely used communication protocols in SCADA systems. They aimed to detect attacks and minimize their effects with the security mechanisms developed with neural network methods [18]. [PERSON] et al. carried out a study on the security of industrial control systems and critical infrastructures. They emphasized that traditional attack detection systems could not detect attacks newly developed and unregistered in databases. They used data from a water distribution system in France in the study and proposed machine learning algorithms for attack detection [19]. Focusing on the developments in information and communication technologies, [PERSON] et al. have emphasized that the complexity and security vulnerabilities in SCADA procedures are gradually increasing. They stated that new security measures were necessary for new-generation SCADA designs integrated into the internet and different systems. Therefore, they proposed an attack detection system with a behavior-based and multilayer framework [20]. [PERSON] et al. proposed two approaches to detect attacks against SCADA systems. The first was to determine whether the data in the system were consistent or inconsistent. The second approach was to obtain proximity detection rules from specified situations. They stated that the KNN-based attack detection system showed significant accuracy [21]. [PERSON] proposed techniques based on temporal pattern recognition for cyberattack detections in SCADA systems. The study proposed two algorithms based on Hidden Markov models (HMM) and artificial neural network-based self-organizing maps (ANN-based SOM). According to the results obtained, they stated that it was easier to detect cyberattacks [22]. [PERSON] et al. discussed temporally uncorrelated and correlated attacks against SCADA systems. They detected attacks using the feedforward neural network (FNN) and LSTM algorithms based on deep learning. The FNN-LSTM model, on the other hand, succeeded in detecting both types of cyberattacks, regardless of their temporal correlations [23]. While intrusions into SCADA systems will continue, defense mechanisms against different attack vectors remain insufficient. Therefore, [PERSON] et al. conducted a study to ensure the cybersecurity of SCADA systems. Accordingly, they proposed an integrated attack detection mechanism against cyberattacks that captures network traffic, divides traffic by source, and creates a set of one-class support vector machine (OCSVM) models [24]. [PERSON] et al. proposed two models aimed at detecting attacks against SCADA systems. These models have many-to-many (MTM) and many-to-one (MTO) architectures. The models used the LSTM algorithm. Both detection systems performed well in detecting temporally uncorrelated attacks [25]. There are many studies aimed at detecting unauthorized access to SCADA systems. [PERSON] and [PERSON] developed an intrusion detection system that classifies attacks based on optimization. They proposed intrusion weighted particle-based cuckoo search optimization (IWP-CSO) and hierarchical neuron architecture-based neural network (HNA-NN) techniques [26; 27]. This study focused on DDoS attacks in SCADA architecture and presented models that work efficiently to detect attacks.