## 2 Related Work  
In [PERSON] et al. [12; 13; 14], the authors use NLP methods and neural networks in an attempt to detect social engineering attacks. They describe a method where offline or online text documents are processed using NLP tools and through artificial neural networks, they discriminate whether the text is a social engineering attack or not. The text in the first stage is parsed and checked for syntactical/grammatical errors using natural language techniques while later an artificial neural network classifies possible social engineering attacks. The proposed method has presented high accuracy results during the evaluation where a real and a semi-synthetic dataset were used for the model training. Furthermore, several other classification models have been tried to compare the two datasets. In [15] by [PERSON] et al., a URL classifier is described based on Random Forest models and gradient boosting classifier. The URL classifier, in order to improve the algorithm's efficiency to detect malicious web sites, makes use of features related to the host and the linguistic characteristics of the URL. By using machine learning algorithms, the authors succeed to drastically reduce the detection time of a malicious URL. Thus, they offer real-time protection for harmless web browsing while at the same time saving computational resources. [PERSON] et al. [16] present a method to detect malicious statements using natural language processing techniques. They analyse the statements and discriminate the malicious ones that imply a phishing attack. The malicious intent of the statements is detected by analysing the statements. The proposed algorithm is evaluated using a phishing email dataset that is used as a benchmark set. [PERSON] et al. in [17] describe a method to represent the syntactic and semantic characteristics of the natural language by using a pre-trained BERT model. The proposed model seems to be resilient to adversarial attacks where the attackers intentionally replace the keywords with synonyms. [PERSON] [18] proposes a social engineering detection model based on deep neural network. The model is able to detect deception attempts and phishing attempts by analysing the text content. The chat history in the first stage is processed and analysed using natural language techniques and the context semantics are captured and mined using a bi-directional Long Short-Term Model (bi-LSTM). The integration of the user characteristics and chat content characteristics as features for the classification is done by ResNet. In [19], the authors detect malicious documents using a framework that is based on machine learning algorithms. A Random Forests ensemble classifier is used which selects in random the features for each classification tree. The features are selected and extracted from the document's metadata and structure. This Random Forests classifier appears to be resilient against mimicry attacks and highly efficient in detection rate. Chatbots can also be utilized for the purpose of recognizing a malicious act such as a CSE attack. In [20], the authors describe a system that protects against CSE attacks by deploying a pipeline of NLP components. The NLP components include NER, Dialogue Engineering, Stylometry and Ask and Framing Question. They use an active defence approach to detect the social engineer's intention and then waste her time and resources. In [21], the authors also present a different approach with a chatbot that is dedicated to educate the users and raise their awareness regarding the social engineering attacks. The chatbot first performs an assessment of cybersecurity concepts knowledge of the user using a quiz, and then based on the answers it proposes specific training paths to fill the knowledge gaps. During the education, the chatbot makes use of malicious questions, in an attempt to extract sensitive data from the users to make them more aware of the possible dangers. Recent trends in social engineering attack recognition utilize AI, ML and NLP tools and techniques to empower their efficiency.