get first paper and add meta-review

trust/EMSOFT24/related_work.tex

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+\section{Related Work}
+\label{sec:related_work}
+
+Cybersecurity requires information from the protected system to evaluate the integrity of a system properly.
+Whether the goal is detection or prevention, the security system relies on quality and trustworthy source information to provide the user with helpful results.
+Among the classic sources of information, such as log files, source code, or network traffic, side-channel information provides compelling insights into a system's activity.
+Historically leveraged for attacks, side channels are directly correlated with the activity of a system and can be considered as a source of information like any other for defense.
+
+Side-channel information is complementary to host or network information and possesses specific characteristics.
+Side-channel can take many form but power consumption in particular is often leverages to obtain side-channel information \cite{randolph2020power}.
+The independent and hard-to-forge nature of involuntary emissions increases the trustworthiness of the information and makes deployment and retrofitting to various machines possible.
+However, the time series data containing the information in its raw form contains unlabeled measurements prone to noise.
+Thus, tailored prepossessing of the raw time series is crucial when leveraging side-channel information.
+
+After prepossessing, several methods can extract information from unlabeled time series.
+A common usage is anomaly detection.
+Anomaly detection systems are often capable of ingesting large datasets of unlabeled data.
+The literature in this domain provides examples of applications to smart grid \cite{9062014}, industrial control systems \cite{DUNLAP201612} or \ac{iot} devices \cite{8653533}.
+
+Another approach is the classification or recognition of time series patterns.
+The pattern can be known-malicious and associated with malware activity \cite{8854845}.
+On the opposite, the pattern can be known-good, and the protection system detects deviations from it \cite{10430037}.
+
+Uncertainty in classification can occur due to noise in side-channel data.
+Users must base their trust in the system on the classifications derived from these indirect observations.
+\textit{Trust} refers to the level of trust that users perceive in the system or technology with which they are engaging.
+A considerable amount of research focuses on the importance of trust in human-computer interactions, particularly in areas such as automation~\cite{akash2020toward, sheng2019case}, robotics~\cite{xu2016maintaining}, aviation~\cite{okamura2018adaptive}, and military~\cite{tomsett2020rapid}.
+Efforts have also been made to establish trust frameworks that calibrate trust levels, as user trust can be influenced by elements such as system reliability, openness, error management, and interaction~\cite{basu2016trust, tomsett2020rapid, kok2020trust, mcdermott2019practical, de2023mutually, kohn2021measurement, akash2020toward}.
+The findings of these studies indicate that most research on trust relies on subjective measures or user-defined criteria to assess trust specific to their studies~\cite{brzowski2019trust}.
+Subjective measures for calculating trust are either self-reporting --- for example, filling out trust questionnaires like the Muir questionnaire~\cite{basu2016trust} --- or implicit measures, for example, observing user behavior, physiological responses, and facial expressions during interactions with the system.
+%A study found that real-time trust cannot be reflected by traditional trust questionnaires such as Muir questionnaire~\cite{basu2016trust}.
+
+\ac{sl}~\cite{josang2016subjective} is a mathematical framework for logical reasoning that accommodates uncertainty through subjective opinions.
+\ac{sl} integrates probabilistic logic with the \ac{dst} of evidence~\cite{shafer1992dempster}, enabling the representation of uncertainty in real-world scenarios and trust modelling in distributed systems.
+It facilitates trustworthiness evaluations via a probabilistic epistemic logic.
+\ac{sl} defines multiple operators to combine opinions from diverse sources in various manners.
+
+Subjective Logic has been applied to assess trust in autonomous driving~\cite{du2023scalable}, transportation infrastructure~\cite{cheng2021trust}, autonomous multi-agent systems~\cite{petrovska2020knowledge, cheng2021general} and \ac{iot} ~\cite{akhuseyinoglu2020automated}.
+These studies mainly employed \ac{sl} in a binary field to analyze evidence, where the observation is classified as either true or false.
+However, they have not addressed situations in which the classification of indirect observations is labelled as uncertain, switching from a binary to a ternary field.
+
+ However, \ac{sl} encounters challenges when analyzing opinions in complex network structures as it necessitates simplifying the network graph, leading to information loss~\cite{liu2014assessment}.
+ \ac{3vsl}~\cite{liu2014assessment} proposes another formalism to compute trust based on an arbitrary opinion graph, which characterizes trust as a three-part event (belief, distrust, uncertain).
+ \ac{3vsl} evaluates trust within intricate networks, such as social networks~\cite{liu2019trust, cheng2019trust}.
+ However, certain operators in the \ac{sl} formalism are not defined in \ac{3vsl}.
+ For instance, the cumulative fusion operator merges opinions on the same proposition across non-overlapping observations.