# Leveraging Dialogue State Tracking for Zero-Shot Chat-Based Social Engineering Attack Recognition
## 3 Background  
### Dialogue Systems  
According to [PERSON] [16], human-to-human dialogues possess several properties. A crucial structural attribute is a turn, which is a singular contribution made by one speaker in the dialogue. A full dialogue comprises a sequence of turns. For instance, in Table 1, two interlocutors identified as V(ictim) and A(tracker) exchange utterances in a small six-turn dialogue excerpted from the CSE corpus [17]. One or more utterances from the same speaker can be grouped into a single turn. Typically, an utterance from speaker V is followed by a response from speaker A, constituting an exchange. A dialogue consists of multiple exchanges.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Turn** & **Utterance** \\ \hline V1 & We don’t allow Telnet, especially from the Internet; it’s not secure. \\  & If you can use SSH, that’d be okay \\ A2 & Yeah, we have SSH. \\ A3 & So, what’s the IP address? \\ V4 & IP is [ANON] \\ A5 & Username and password? \\ V6 & Username is [ANON] and password is [ANON] \\ \hline \hline \end{tabular}
\end{table}
Table 1: A sample dialogue in turns.  
Nowadays, a dialogue can be conducted between humans or between a machine and a human, and the latter is named a dialogue system. Dialogue systems communicate with humans in natural language, both spoken and written, and can be classified as:  
* Task-based dialogue systems where the system helps humans to complete a task.
* Conversational dialogue systems where the systems answer questions. Task-based systems can be further classified, based on their architecture, into systems that use:
* Genial Understander System (GUS) architecture [18] which is an old simple approach to describing a dialogue structure.
* Dialogue-State (DS) architecture, in which the dialogue is modeled as a series of different states.  
The primary objective of dialogue systems when interacting with a human is to elicit three key pieces of information from the user's utterance: domain classification, user intention, and requested information. Domain classification pertains to the conversation's context and can be determined through the use of a domain ontology. To ensure proper interpretation and response to user input, a formal representation of the knowledge that a conversational system must comprehend must exist. This knowledge comprises concepts and entities mentioned in the conversation, the relationships between them, and the potential dialogue states, which are encapsulated in the domain ontology. Dialogue systems rely on ontologies to link the user's input to the corresponding concepts and entities, as well as to monitor the current dialogue state. By tracking the state of the conversation and the user's objectives, the system can generate more precise and pertinent responses. The domain ontology can be defined by means of a static schema, where the dialogue system operates within a single domain, or a dynamic schema, where the dialogue system is capable of operating within multiple domains. In this context, the schema represents a framework or structure that defines the arrangement of data. Typically, a dialogue system consists of the following units:  
* Natural Language Understanding (NLU) unit, which uses machine learning to interpret the user's utterance.
* Dialogue-State Tracking (DST) unit, which maintains the entire history of the dialogue.
* Dialogue Policy unit, which defines a set of rules to drive the interactions between the user and the system.
* Natural Language Generation (NLG), which generates responses in natural language.
* Text-to-Speech unit, which transforms text to speech.
* Automated Speech Recognition unit, which transforms speech into text.