49 lines
7.4 KiB
TeX
49 lines
7.4 KiB
TeX
% !TEX root = ../Thesis.tex
|
|
\chapter{Background}
|
|
This section provides the necessary background to understand the foundational concepts related to IoT devices, testbeds, and data principles that inform the design and implementation of \iottbsc.
|
|
|
|
\section{Internet of Things}
|
|
The \iot refers to the connection of “things” other than traditional computers to the internet. The decreasing size of microprocessors has enabled their integration into smaller and smaller objects. Today, objects like security cameras, home lighting, or children's toys may contain a processor and embedded software that enables them to interact with the internet. The Internet of Things encompasses objects whose purpose has a physical dimension, such as using sensors to measure the physical world or functioning as simple controllers. When these devices can connect to the internet, they are considered part of the Internet of Things and are referred to as \textbf{IoT devices} (see \citet{whatissmartdevice2018} and \citet{iotfundamentals}).
|
|
|
|
\section{Testbed}
|
|
A testbed is a controlled environment set up to perform experiments and tests on new technologies. The concept is used across various fields such as aviation, science, and industry. Despite the varying contexts, all testbeds share the common goal of providing a stable, controlled environment to evaluate the performance and characteristics of the object of interest.
|
|
|
|
Examples of testbeds include:
|
|
\begin{enumerate}
|
|
\item \textbf{Industry and Engineering}: In industry and engineering, the term \emph{platform} is often used to describe a starting point for product development. A platform in this context can be considered a testbed where various components and technologies are integrated and tested together before final deployment.
|
|
\item \textbf{Natural Sciences}: In the natural sciences, laboratories serve as testbeds by providing controlled environments for scientific experiments. For example, climate chambers are used to study the effects of different environmental conditions on biological samples (e.g., in \citet{vaughan2005use}). Another example is the use of wind tunnels in aerodynamics research to simulate and study the effects of airflow over models of aircraft or other structures.
|
|
\item \textbf{Computing}: In computing, specifically within software testing, a suite of unit tests, integrated development environments (IDEs), and other tools could be considered as a testbed. This setup helps in identifying and resolving potential issues before deployment. By controlling parameters of the environment, a testbed can ensure that the software behaves as expected under specified conditions, which is essential for reliable and consistent testing.
|
|
\item \textbf{Interdisciplinary}: Testbeds can take on considerable scales. For instance, in \citet{tbsmartgrid2013} provides insight into the aspects of a testbed for a smart electric grid.
|
|
This testbed is composed out of multiple systems, — an electrical grid, internet, and communication provision — which in their own right are already complex environments.
|
|
The testbed must, via simulation or prototyping, provide control mechanisms, communication, and physical system components.
|
|
|
|
\end{enumerate}
|
|
|
|
|
|
\section{FAIR Data Principles}
|
|
\label{concept:fair}
|
|
The \emph{FAIR Data Principles} were first introduced by \citet{wilkinson_fair_2016} with the intention to improve the reusability of scientific data. The principles address \textbf{F}indability, \textbf{A}ccessibility, \textbf{I}nteroperability, and \textbf{R}eusability. Data storage designers may use these principles as a guide when designing data storage systems intended to hold data for easy reuse.
|
|
For a more detailed description, see \citep{go-fair}.
|
|
|
|
\section{Network Traffic}\label{sec:network-traffic}
|
|
Studying \iot devices fundamentally involves understanding their network traffic behavior.
|
|
This is because network traffic contains (either explicitly or implicitly embedded in it) essential information of interest.
|
|
Here are key reasons why network traffic is essential in the context of \iot device security:
|
|
\begin{enumerate}
|
|
\item \textbf{Communication Patterns}: Network traffic captures the communication patterns between IoT devices and external servers or other devices within the network. By analyzing these patterns, researchers can understand how data flows in and out of the device, which is critical for evaluating performance and identifying any unauthorized communications or unintended leaking of sensitive information.
|
|
\item \textbf{Protocol Analysis:} Examining the protocols used by IoT devices helps in understanding how they operate. Different devices might use various communication protocols, and analyzing these can reveal insights into their compatibility, efficiency, and security. Protocol analysis can also uncover potential misconfigurations or deviations from expected behavior.
|
|
\item \textbf{Flow Monitoring:} Network traffic analysis is a cornerstone of security research. It allows researchers to identify potential security threats such as data breaches, unauthorized access, and malware infections. By monitoring traffic, one can detect anomalies that may indicate security incidents or vulnerabilities within the device.
|
|
\item \textbf{Information Leakage}: \iot devices are often deployed in a home environment and connect to the network through wireless technologies \citep{iothome2019}. This allows an adversary to passively observe traffic. While often this traffic is encrypted, the network flow can leak sensitive information, which is extracted through more complex analysis of communication traffic and Wi-Fi packets \citep{friesssniffing2018}, \citep{infoexpiot}. In some cases, the adversary can determine the state of the smart environment and their users \citep{peekaboo2020}.
|
|
\end{enumerate}
|
|
|
|
|
|
\section{(Network) Packet Capture}
|
|
Network \textit{packet capture} \footnote{also known as \emph{packet sniffing}, \emph{network traffic capture}, or just \emph{sniffing}. The latter is often used when referring to nefarious practices.} fundamentally describes the act or process of intercepting and storing data packets traversing a network. It is the principal technique used for studying the behavior and communication patterns of devices on a network. For the reasons mentioned in \cref{sec:network-traffic}, packet capturing is the main data collection mechanism used in \iot device security research, and also the one considered for this project.
|
|
|
|
\section{Automation Recipes}
|
|
\todoRevise()
|
|
Automation recipes can be understood as a way of defining a sequence of steps needed for a process.
|
|
In the field of machine learning, \textit{Collective Mind}\footnote{\url{https://github.com/mlcommons/ck}} provides a small framework to define reusable recipes for building, running, benchmarking and optimizing machine learning applications.
|
|
A key aspect of these recipes some platform-independent, which has enabled wider testing and benchmarking of machine learning models. Even if a given recipe is not yet platform independent, it can be supplemented with user-specific scripts which handle the platform specifics. Furthermore, it is possible to create a new recipe from the old recipe and the new script, which, when made accessible, essentially has extended the applicability of the recipe \citet{friesssniffing2018}.
|
|
Automation recipes express the fact that some workflow is automated irrespective of the underlying tooling. A simple script or application can be considered an recipe (or part of)
|