## 1 Introduction  
SMEs remain one of the most popular targets for cyber-attacks. It is also a fact employees nowadays are extensively using Information and Communication Technologies (ICT) and especially Electronic Medium Communication (EMC) software for almost every aspect of their daily activities. Although EMC software is an advantageous tool for the employees, it also constitutes a large and vulnerable attack surface. The vulnerabilities lay in human personality characteristics, and the way adversaries exploit them to extort valuable information for their benefit. As stated by ENISA [1], the term Social Engineering \"refers to all techniques aimed at talking a target into revealing specific information or performing a specific action for illegitimate reasons\". Researchers performed several successful classifications of the social engineering attacks [2, 3, 4] based on criteria such as the entity involved, the medium used to unleash the attack or the number of steps an attack can take. All these classifications end up in fine-grained taxonomies that include different methods of social engineering attacks. Verizon in [5] reports that in 2020 social engineering attacks were at 21%, the first step of every cyber-attack that led to a major data breach. Furthermore, the same report mentions that social engineering attackers' methods of choice are phishing and pretexting. The pretext method uses an invented scenario to facilitate the attacker to persuade the employee to do what the attacker wants [4]. Social engineering attacks are not only unleashed by humans: recent advances in Chatbot technology escalate even more the problem of social engineering. Chatbots [6]are conversational agents that are communicating through artificial intelligence (AI) and natural language processing (NLP). A chatbot dedicated to accomplishing a malicious task can utilize multiple personalities in an attempt to deceive an SME employee and extract sensitive data. Chatbots are also capable of approaching all types of humans presenting different personalities based on the six principles of persuasion defined by [PERSON] [7] (reciprocity, scarcity, authority, commitment, liking, and social proof). Moreover, due to the COVID-19 pandemic the use of conversational agents increased, and it is a rather common practice for an employee to socialize with a chatbot as part of daily routine operations. SME employees must be aware of the possible dangers and be protected from malicious questions which aim to extract sensitive data. Although chatbots are a novel tool in the hands of social engineers, the rapid pace in AI and NLP makes them an important factor due to their capabilities. Today, the majority of social engineering attacks are operated by humans, but AI-driven social engineering attacks are expected to happen in the near future through chatbots too [8]. As employees use chat-based software to offer their services, this can become dangerous if a social engineer decides to unleash a sophisticated attack using a pretext scenario during an online conversation. A chat-based social engineering (CSE) attack cannot be described using only technical terms due to the nature of the human vulnerabilities. Thus, we need a multi-factor recognition system to automatically detect a CSE attack. An ideal multi-factor recognizer should process the dialogue in real time and protect the employee by predicting potential CSE attacks in early stages, e.g., by alerting the employee if the probability of sensitive data leakage exceeds a predefined threshold. Such a prediction can be based either on a pure statistical approach or a machine learning (ML) approach. Considering the importance of recognizing a CSE attack in real-time, the ML approach can qualify as an efficient solution. Furthermore, ML algorithms can be combined perfectly with NLP algorithms which can be used to process the language used in a natural communication setting. Such ML/NLP algorithms need labelled datasets to be trained and to be efficient in their predictions.