## 3 Related Work  
The integration of Artificial Intelligence (AI) into IDSs for IoT systems has been the focus of academic and commercial research efforts in recent years. Evidence suggests the combination of the two is most effective in identifying potentially malicious patterns of behavior. Machine learning methods such as Naive Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) have all seen widespread usage in the detection of network-based threats. The methods are all based on the concept of learning from a labeled dataset. This is a common feature of data flowing between IoT devices. Deep Learning approaches have been the subject of further studies to increase accuracy and mitigate the effects of feature selection. The model's approach and data collection are essential in determining the relevance of its findings to the real world. Major online, mobile, and firmware zero-day vulnerabilities were discovered in an analysis of sixteen popular EVCSMS used by well-known companies, particularly in Europe and the United States [9]. They used the discovered vulnerabilities to gain unauthorized access to the EVCS, which caused power outages for the system's end customers. When compared to the security of other components of the EV ecosystem, the firmware and EVCSMS of EVCSs have received less attention from academics. The research in [10] quantified the severity of potential vulnerabilities in an operational EVCS. According to their results, there is a serious vulnerability in the live systems now used to charge EVs, which might have serious repercussions for the power grid and its consumers. Their study concluded with a set of preventative actions that may be implemented in vulnerable systems to decrease the impact of such attacks. The authors in [11] proposed building an intrusion detection system using Deep Belief Networks (DBNs). It is a technique for enhancing the input to successive layers of architecture made up of different unsupervised networks. To do this, they employed auto encoders, specifically confined Boltzmann Machines (RBMs). After completing the training, they labeled events as either 0 (no intrusion detected) or 1 (intrusion detected). The 30,000-tuple TON IOT dataset was used in this model. This IoT dataset was developed in a controlled setting by the UNSW Canberra Cyber Range and IoT labs to mimic a medium-sized network in Australia. This dataset is known as the go-to source for a comprehensive collection of non-standard Internet of Things (IoT) risks. TensorFlow was used to create the  
Figure 3: Security requirements and their corresponding attacks in EVCS. model's code. The data show that this model is 84% effective and has an F1 score of 84%. A notable aspect of the study is the comparison of DBN results to those acquired by other algorithms, which reveals that the DBN has lower accuracy (86%) than the Deep Neural Network (DNN) (96%) and the LSTM + CNN (97%), although performs better than the NB (54%) and the Support Vector Machine (SVM) (97%). [PERSON] et al. (2019) completed a comprehensive study on the usage of machine learning and deep learning techniques in IoT intrusion detection systems. Security issues and risks to the Internet of Things systems were outlined. Since denial-of-service (DoS) attacks inside the EVCS are a real possibility owing to the IoT system's open communication layer, the authors of ([PERSON] et al., 2019) developed a deep-learning-based Intrusion Detection System (IDS) to detect them. They combined the DNN and Long Short-Term Memory (LSTM) neural network learning techniques. There was a 99% success rate with both approaches; however, the study concluded that the LSTM was superior. However, the research was narrowly focused on the Distributed Denial of Service (DDoS) attack. Using deep learning in a real-time setting, as suggested in ([PERSON] et al., 2019), would be a waste of resources and, at most, would only uncover malicious behaviors after the traffic had already been routed across the network.