% Chapter 1

\chapter{Introduction} % Main chapter title

\label{Introduction} % For referencing the chapter elsewhere, use \ref{Chapter1} 

%----------------------------------------------------------------------------------------

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%----------------------------------------------------------------------------------------

%This part is about regular expressions, Brzozowski derivatives,

%and a bit-coded lexing algorithm with proven correctness and time bounds.

%TODO: look up snort rules to use here--give readers idea of what regexes look like

Regular expressions are widely used in computer science: 

be it in text-editors \parencite{atomEditor} with syntax highlighting and auto-completion;

command-line tools like $\mathit{grep}$ that facilitate easy 

text-processing \cite{grep}; network intrusion

detection systems that inspect suspicious traffic; or compiler

front ends.

Given their usefulness and ubiquity, one would assume that

modern regular expression matching implementations

are mature and fully studied.

Indeed, in a popular programming language's regular expression engine, 

supplying it with regular expressions and strings,

in most cases one can

get the matching information in a very short time.

Those engines can be blindingly fast--some 

network intrusion detection systems

use regular expression engines that are able to process 

hundreds of megabytes or even gigabytes of data per second \parencite{Turo_ov__2020}.

However, those engines can sometimes exhibit a surprising security vulnerability

under a certain class of inputs.

%However, , this is not the case for $\mathbf{all}$ inputs.

%TODO: get source for SNORT/BRO's regex matching engine/speed

Consider for example $(a^*)^*\,b$ and 

strings of the form $aa..a$, these strings cannot be matched by this regular

expression: Obviously the expected $b$ in the last

position is missing. One would assume that modern regular expression

matching engines can find this out very quickly. Surprisingly, if one tries

this example in JavaScript, Python or Java 8, even with small strings, 

say of lenght of around 30 $a$'s,

the decision takes an absurd amount of time to finish (see graphs in figure \ref{fig:aStarStarb}).

This is clearly exponential behaviour, and as can be seen

is triggered by some relatively simple regular expressions.

Java 9 and newer

versions improve this behaviour somewhat, but are still slow compared 

with the approach we are going to use in this thesis.

This superlinear blowup in regular expression engines

has caused grief in ``real life'' in the past where it is 

given the name ``catastrophic backtracking'' or ``evil'' regular expressions.

For example, on 20 July 2016 one evil

regular expression brought the webpage

\href{http://stackexchange.com}{Stack Exchange} to its

knees.\footnote{\url{https://stackstatus.net/post/147710624694/outage-postmortem-july-20-2016}(Last accessed in 2019)}

In this instance, a regular expression intended to just trim white

spaces from the beginning and the end of a line actually consumed

massive amounts of CPU resources---causing the web servers to grind to a

halt. In this example, the time needed to process

the string was 

$O(n^2)$ with respect to the string length $n$. This

quadratic overhead was enough for the homepage of Stack Exchange to

respond so slowly that the load balancer assumed a $\mathit{DoS}$ 

attack and therefore stopped the servers from responding to any

requests. This made the whole site become unavailable. 

\begin{figure}[p]

\begin{center}

\begin{tabular}{@{}c@{\hspace{0mm}}c@{}}

\begin{tikzpicture}

\begin{axis}[

    xlabel={$n$},

    x label style={at={(1.05,-0.05)}},

    ylabel={time in secs},

    enlargelimits=false,

    xtick={0,5,...,30},

    xmax=33,

    ymax=35,

    ytick={0,5,...,30},

    scaled ticks=false,

    axis lines=left,

    width=5cm,

    height=4cm, 

    legend entries={JavaScript},  

    legend pos=north west,

    legend cell align=left]

\addplot[red,mark=*, mark options={fill=white}] table {re-js.data};

\end{axis}

\end{tikzpicture}

  &

\begin{tikzpicture}

\begin{axis}[

    xlabel={$n$},

    x label style={at={(1.05,-0.05)}},

    %ylabel={time in secs},

    enlargelimits=false,

    xtick={0,5,...,30},

    xmax=33,

    ymax=35,

    ytick={0,5,...,30},

    scaled ticks=false,

    axis lines=left,

    width=5cm,

    height=4cm, 

    legend entries={Python},  

    legend pos=north west,

    legend cell align=left]

\addplot[blue,mark=*, mark options={fill=white}] table {re-python2.data};

\end{axis}

\end{tikzpicture}\\ 

\begin{tikzpicture}

\begin{axis}[

    xlabel={$n$},

    x label style={at={(1.05,-0.05)}},

    ylabel={time in secs},

    enlargelimits=false,

    xtick={0,5,...,30},

    xmax=33,

    ymax=35,

    ytick={0,5,...,30},

    scaled ticks=false,

    axis lines=left,

    width=5cm,

    height=4cm, 

    legend entries={Java 8},  

    legend pos=north west,

    legend cell align=left]

\addplot[cyan,mark=*, mark options={fill=white}] table {re-java.data};

\end{axis}

\end{tikzpicture}

  &

\begin{tikzpicture}

\begin{axis}[

    xlabel={$n$},

    x label style={at={(1.05,-0.05)}},

    %ylabel={time in secs},

    enlargelimits=false,

    xtick={0,5,...,30},

    xmax=33,

    ymax=35,

    ytick={0,5,...,30},

    scaled ticks=false,

    axis lines=left,

    width=5cm,

    height=4cm, 

    legend entries={Dart},  

    legend pos=north west,

    legend cell align=left]

\addplot[green,mark=*, mark options={fill=white}] table {re-dart.data};

\end{axis}

\end{tikzpicture}\\ 

\begin{tikzpicture}

\begin{axis}[

    xlabel={$n$},

    x label style={at={(1.05,-0.05)}},

    ylabel={time in secs},

    enlargelimits=false,

    xtick={0,5,...,30},

    xmax=33,

    ymax=35,

    ytick={0,5,...,30},

    scaled ticks=false,

    axis lines=left,

    width=5cm,

    height=4cm, 

    legend entries={Swift},  

    legend pos=north west,

    legend cell align=left]

\addplot[purple,mark=*, mark options={fill=white}] table {re-swift.data};

\end{axis}

\end{tikzpicture}

  & 

\begin{tikzpicture}

\begin{axis}[

    xlabel={$n$},

    x label style={at={(1.05,-0.05)}},

    %ylabel={time in secs},

    enlargelimits=true,

    %xtick={0,5000,...,40000},

    %xmax=40000,

    %ymax=35,

    restrict x to domain*=0:40000,

    restrict y to domain*=0:35,

    %ytick={0,5,...,30},

    %scaled ticks=false,

    axis lines=left,

    width=5cm,

    height=4cm, 

    legend entries={Java9+},  

    legend pos=north west,

    legend cell align=left]

\addplot[orange,mark=*, mark options={fill=white}] table {re-java9.data};

\end{axis}

\end{tikzpicture}\\ 

\multicolumn{2}{c}{Graphs}

\end{tabular}