Content

codes/frequency_deriv/dummy.log.old

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codes/frequency_deriv/frequency_deriv.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 
+import statistics
 from collections import defaultdict
 from typing import Dict
 import matplotlib.pyplot as plt
@@ -18,13 +19,18 @@ def load_log(path: str) -> Dict[datetime, str]:
 
 
 
-def plot_deriv(data: Dict[datetime, str]):
+def plot_devi(data: Dict[datetime, str]):
     diffs = []
     per_crawler = defaultdict(list)
     sor = list(sorted(data.items(), key=lambda kv: kv[0]))
+    # c = 0
+    per_diff = defaultdict(list)
     for prev, next in zip(sor, sor[1:]):
-        diffs.append(abs(2.5 - (next[0].timestamp() - prev[0].timestamp())))
+        diff = abs(2.5 - (next[0].timestamp() - prev[0].timestamp()))
+        diffs.append(diff)
         per_crawler[prev[1]].append(prev[0])
+        per_diff[prev[1]].append(diff)
+        # c = (c + 1) % 4
 
 
     # expected = [2.5] * len(diffs)
@@ -32,15 +38,34 @@ def plot_deriv(data: Dict[datetime, str]):
     # x = []
     x = [2.5 * x for x in range(len(diffs))]
     fig, ax = plt.subplots()
-    ax.set_title('Timedelta between crawl events in seconds')
+    ax.set_title('Deviation between crawl events')
     # ax.set_ylabel()
     ax.set_xlabel('Time passed in seconds')
     ax.set_ylabel('Deviation in seconds')
     # ax.plot(x, expected, label='Expected difference')
-    ax.plot(x, diffs, label='Deviation from the expected value')
+    ax.scatter(x, diffs, label='Deviation from the expected value', s=10)
     fig.legend()
     # plt.show()
-    plt.savefig('./time_deriv.png')
+    plt.savefig('./time_devi.png')
+    plt.close()
+
+    # x = [2.5 * x for x in range(len(diffs))]
+    fig, ax = plt.subplots()
+    ax.set_title('Deviation between crawl events')
+    # ax.set_ylabel()
+    ax.set_xlabel('Time passed in seconds')
+    ax.set_ylabel('Deviation in seconds')
+    # ax.plot(x, expected, label='Expected difference')
+    for c, vals in per_diff.items():
+        # if not c in ['c0', 'c3']:
+        #     continue
+        x = [10 * x for x in range(len(vals))]
+
+        n = int(c[1:])
+        ax.scatter(x, vals, label=f'Deviation between c{n} and c{(n+1)%4}', s=10)
+    fig.legend()
+    # plt.show()
+    plt.savefig('./xxx.png')
     plt.close()
 
     for c in per_crawler.keys():
@@ -50,20 +75,22 @@ def plot_deriv(data: Dict[datetime, str]):
             devi.append(abs(10 - (nex.timestamp() - pre.timestamp())))
         x = [10 * x for x in range(len(devi))]
         fig, ax = plt.subplots()
-        ax.plot(x, devi)
+        ax.scatter(x, devi, s=10)
         ax.set_title(f'Timedeviation for {c}')
         ax.set_xlabel('Time passed in seconds')
         ax.set_ylabel('Deviation in seconds')
-        plt.savefig(f'./time_deriv_{c}.png')
+        plt.savefig(f'./time_devi_{c}.png')
         plt.close()
+        print(f'{c}: {statistics.mean(devi)}')
         # for ts in per_crawler[c]:
 
 
 
 
+
 def main():
     data = load_log('./dummy.log')
-    plot_deriv(data)
+    plot_devi(data)
 
 
 if __name__ == '__main__':

content.tex

@@ -27,7 +27,7 @@ A coordinated operation with help from law enforcement, hosting providers, domai
 
 The monetary value of these botnets directly correlates with the amount of effort botmasters are willing to put into implementing defense mechanisms against take-down attempts.
 Botnet operators came up with a number of ideas: \acp{dga} use pseudorandomly generated domain names to render simple domain blacklist-based approaches ineffective~\cite{bib:antonakakis_dga_2012} or fast-flux DNS entries, where a large pool of IP addresses is randomly assigned to the \ac{c2} domains to prevent IP based blacklisting and hide the actual \ac{c2} servers~\cite{bib:nazario_as_2008}.
-Analyzing and shutting down a centralized or decentralized botnet is comparatively easy since the central means of communication (the \ac{c2} IP addresses or domain names, Twitter handles or \ac{irc} channels), can be extracted from the malicious binaries or determined by analyzing network traffic and can therefore be considered publicly known.
+Analyzing and shutting down a centralized or decentralized botnet is comparatively easy since the central means of communication (the \ac{c2} IP addresses or domain names, Twitter handles, or \ac{irc} channels), can be extracted from the malicious binaries or determined by analyzing network traffic and can therefore be considered publicly known.
 A number of botnet operations were taken down by shutting down the \ac{c2} channel~\cite{bib:nadji_beheading_2013, bib:msZloader} and as the defenders upped their game, so did attackers---the concept of \ac{p2p} botnets emerged.
 The idea is to build a distributed network without \acp{spof} in the form of \ac{c2} servers as shown in \Fref{fig:p2p}.
 
@@ -92,8 +92,8 @@ This is achieved by periodically querying their neighbor's neighbors in a proces
 
 \Ac{mm} can be distinguished into two categories: \emph{structured} and \emph{unstructured}~\cite{bib:baileyNextGen}.
 Structured \ac{p2p} botnets have strict rules for a bot's neighbors based on its unique ID and often use a \ac{dht}, which allows persisting data in a distributed network.
-The \ac{dht} could contain a ordered ring structure of IDs and neighborhood in the structure also means neighborhood in the network, as is the case in the Kademila botnet~\cite{bib:kademlia2002}.
+The \ac{dht} could contain an ordered ring structure of IDs and neighborhood in the structure also means neighborhood in the network, as is the case in the Kademila botnet~\cite{bib:kademlia2002}.
-In \ac{p2p} botnets that employ unstructured \ac{mm} on the other hand, bots ask any peer they know for new peers to connect to, in a process called \emph{peer discovery}.
+In \ac{p2p} botnets that employ unstructured \ac{mm}, on the other hand, bots ask any peer they know for new peers to connect to, in a process called \emph{peer discovery}.
 To enable peers to join a unstructured \ac{p2p} botnets, the malware binaries include hardcoded lists of superpeers for the newly infected systems to connect to.
 
 The concept of \emph{churn} describes when a bot becomes unavailable.
@@ -111,7 +111,7 @@ There are two types of churn:
 %{{{ formal model
 \subsection{Formal Model of \Acs*{p2p} Botnets}
 
-A \ac{p2p} botnet can be modelled as a digraph
+A \ac{p2p} botnet can be modeled as a digraph
 
 \begin{align*}
 G &= (V, E)
@@ -363,7 +363,7 @@ Load balancing allows scaling out, which can be more cost-effective.
 
 This strategy distributes work evenly among crawlers by either naively assigning tasks to the crawlers rotationally or weighted according to their capabilities\todo{1 -- 2 sentences about naive rr?}.
 To keep the distribution as even as possible, we keep track of the last crawler a task was assigned to and start with the next in line in the subsequent round of assignments.
-For the sake of simplicity only the bandwidth will be considered as a capability but it can be extended by any shared property between the crawlers, \eg{} available memory or processing power.
+For the sake of simplicity, only the bandwidth will be considered as a capability but it can be extended by any shared property between the crawlers, \eg{} available memory or processing power.
 For a given crawler \(c_i \in C\) let \(cap(c_i)\) be the capability of the crawler.
 The total available capability is \(B = \sum\limits_{c \in C} cap(c)\).
 With \(G\) being the greatest common divisor of all the crawler's capabilities, the weight \(W(c_i) = \frac{cap(c_i)}{G}\).
@@ -517,7 +517,7 @@ Those crawlers must be scheduled \(o = \frac{\SI{1}{\request}}{\SI{24}{\request\
 
 As can be seen in~\Fref{fig:crawlerTimelineEffective}, each crawler \(C_0\) to \(C_3\) performs only \SI{6}{\request\per\minute} while overall achieving \(\SI{24}{\request\per\minute}\).
 
-Vice versa given an amount of crawlers \(n\) and a request limit \(l\), the effective frequency \(f\) can be maximized to \(f = n \times l\) without hitting the limit \(l\) and being blocked.
+Vice versa, given an amount of crawlers \(n\) and a request limit \(l\), the effective frequency \(f\) can be maximized to \(f = n \times l\) without hitting the limit \(l\) and being blocked.
 
 Using the example from above with \(l = \SI{6}{\request\per\minute}\) but now only two crawlers \(n = 2\), it is still possible to achieve an effective frequency of \(f = 2 \times \SI{6}{\request\per\minute} = \SI{12}{\request\per\minute}\) with \(o = \frac{\SI{1}{\request}}{\SI{12}{\request\per\minute}} = \SI{5}{s}\):
 
@@ -568,7 +568,7 @@ With \(v \in V\), \(\text{succ}(v)\) being the set of successors of \(v\) and \(
 
 For the first iteration, the PageRank of all nodes is set to the same initial value. \citeauthor{bib:page_pagerank_1998} argue that when iterating often enough, any value can be chosen~\cite{bib:page_pagerank_1998}.
 
-The dampingFactor describes the probability of a person visiting links on the web to continue doing so, when using PageRank to rank websites in search results.
+The dampingFactor describes the probability of a person visiting links on the web to continue doing so when using PageRank to rank websites in search results.
 For simplicity---and since it is not required to model human behavior for automated crawling and ranking---a dampingFactor of \(1.0\) will be used, which simplifies the formula to
 
 \[
@@ -584,8 +584,8 @@ Based on this, \emph{SensorRank} is defined as
 
 Since crawlers never respond to peer list requests, they will always be detectable by the described approach but sensors might benefit from the following technique.
 
-The PageRank and SensorRank metric are calculated over the sum of the ranks of a node's predecessors.
+The PageRank and SensorRank metrics are calculated over the sum of the ranks of a node's predecessors.
-We will investigate, how limiting the number of predecessors helps producing inconspicuous ranks for a sensor.
+We will investigate, how limiting the number of predecessors helps produce inconspicuous ranks for a sensor.
 
 % By responding to peer list requests with plausible data and thereby producing valid outgoing edges from the sensors, we will try to make those metrics less suspicious.
 To counter the SensorBuster metric, outgoing edges to valid peers from the botnet are required so the sensor does not build a \ac{wcc}.
@@ -636,11 +636,11 @@ In theory, it would be possible to detect churned peers or peers behind carrier-
 
 \begin{itemize}
 
-  \item A peer might blacklist a sensor which looks exactly the same as a churned peer from the point of view of an uncoordinated sensor.
+  \item A peer might blacklist a sensor that looks exactly the same as a churned peer from the point of view of an uncoordinated sensor.
-    The coordination backend has more knowledge and can detect this, if another sensor is still contacted by the peer in question.
+    The coordination backend has more knowledge and can detect this if another sensor is still contacted by the peer in question.
 
   \item The coordination backend can include different streams of information to decide which peers to place in the sensor's neighborhood.
-    Knowledge about geolocations, \ac{as} and their IP rotation behavior can be consulted to make better informed choices for neighborhood candidates.
+    Knowledge about geolocations, \ac{as} and their IP rotation behavior can be consulted to make better-informed choices for neighborhood candidates.
 
 \end{itemize}
 
@@ -659,83 +659,109 @@ To evaluate the strategies from above, we took a snapshot of the Sality~\cite{bi
 
 To evaluate the real-world applicability of IP based partitioning, we will partition the dataset containing \num{1595} distinct IP addresses among \num{2}, \num{4}, \num{6}, and \num{10} crawlers and verify if the work is about evenly distributed between crawlers.
 
-We will compare the variance \(\sigma^2\) and standard derivation \(\sigma\) to evaluate the applicability of this method.
+We will compare the variance \(\sigma^2\) and standard deviation \(\sigma\) to evaluate the applicability of this method.
 
-%{{{ fig:ipPartC02
+\begin{landscape}
 \begin{figure}[H]
+\begin{subfigure}[b]{.6\textwidth}
 \centering
 \includegraphics[width=1\linewidth]{ip_part_c02.png}
-\caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 2 \\
+\begin{subfigure}[b]{.6\textwidth}
-  \mu &= \frac{1595}{n} = 797.5 \\
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(808 - 797.5)}^2 + {(787 - 797.5)}^2 \\
-    &= 220.5 \\
-  \sigma^2 &= \frac{s}{n} = 110.2 \\
-  \sigma &= \sqrt{\sigma^2} = 10.5
-\end{align*}
-\end{figure}
-%}}}fig:ipPartC02
-
-
-%{{{ fig:ipPartC04
-\begin{figure}[H]
 \centering
 \includegraphics[width=1\linewidth]{ip_part_c04.png}
-\caption{IP based partitioning for 4 crawlers}\label{fig:ipPartC04}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 4 \\
+\hfill
-  \mu &= \frac{1595}{n} = 398.8 \\
+\begin{subfigure}[b]{.6\textwidth}
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(403 - 398.8)}^2 + {(369 - 398.8)}^2 + {(405 - 398.8)}^2 \\
-    &+ {(418 - 398.8)}^2 \\
-    &= 1312.8 \\
-  \sigma^2 &= \frac{s}{n} = 328.2 \\
-  \sigma &= \sqrt{\sigma^2} = 18.1
-\end{align*}
-\end{figure}
-%}}}fig:ipPartC04
-
-
-%{{{ fig:ipPartC06
-\begin{figure}[H]
 \centering
 \includegraphics[width=1\linewidth]{ip_part_c06.png}
-\caption{IP based partitioning for 6 crawlers}\label{fig:ipPartC06}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 6 \\
+\begin{subfigure}[b]{.6\textwidth}
-  \mu &= \frac{1595}{n} = 265.8 \\
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(258 - 265.8)}^2 + {(273 - 265.8)}^2 + {(257 - 265.8)}^2 \\
-    &+ {(264 - 265.8)}^2 + {(293 - 265.8)}^2 + {(250 - 265.8)}^2 \\
-    &= 1182.8 \\
-  \sigma^2 &= \frac{s}{n} = 197.1 \\
-  \sigma &= \sqrt{\sigma^2} = 14.0
-\end{align*}
-
-\end{figure}
-%}}}fig:ipPartC06
-
-%{{{ fig:ipPartC10
-\begin{figure}[H]
 \centering
 \includegraphics[width=1\linewidth]{ip_part_c10.png}
-\caption{IP based partitioning for 10 crawlers}\label{fig:ipPartC10}
+\end{subfigure}%
-\begin{align*}
+\caption{IP based partitioning}\label{fig:ipPart}
-  n &= 10 \\
-  \mu &= \frac{1595}{n} = 159.5 \\
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(140 - 159.5)}^2 + {(175 - 159.5)}^2 + {(186 - 159.5)}^2 \\
-    &+ {(166 - 159.5)}^2 + {(159 - 159.5)}^2 + {(152 - 159.5)}^2 \\
-    &+ {(172 - 159.5)}^2 + {(148 - 159.5)}^2 + {(151 - 159.5)}^2 \\
-    &+ {(146 - 159.5)}^2 \\
-    &= 1964.5 \\
-  \sigma^2 &= \frac{s}{n} = 196.4 \\
-  \sigma &= \sqrt{\sigma^2} = 14.0
-\end{align*}
 \end{figure}
-%}}}fig:ipPartC10
+\end{landscape}
+
+%%{{{ fig:ipPartC02
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{ip_part_c02.png}
+%\caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
+%\begin{align*}
+%  n &= 2 \\
+%  \mu &= \frac{1595}{n} = 797.5 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(808 - 797.5)}^2 + {(787 - 797.5)}^2 \\
+%    &= 220.5 \\
+%  \sigma^2 &= \frac{s}{n} = 110.2 \\
+%  \sigma &= \sqrt{\sigma^2} = 10.5
+%\end{align*}
+%\end{figure}
+%%}}}fig:ipPartC02
+
+
+%%{{{ fig:ipPartC04
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{ip_part_c04.png}
+%\caption{IP based partitioning for 4 crawlers}\label{fig:ipPartC04}
+%\begin{align*}
+%  n &= 4 \\
+%  \mu &= \frac{1595}{n} = 398.8 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(403 - 398.8)}^2 + {(369 - 398.8)}^2 + {(405 - 398.8)}^2 \\
+%    &+ {(418 - 398.8)}^2 \\
+%    &= 1312.8 \\
+%  \sigma^2 &= \frac{s}{n} = 328.2 \\
+%  \sigma &= \sqrt{\sigma^2} = 18.1
+%\end{align*}
+%\end{figure}
+%%}}}fig:ipPartC04
+
+
+%%{{{ fig:ipPartC06
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{ip_part_c06.png}
+%\caption{IP based partitioning for 6 crawlers}\label{fig:ipPartC06}
+%\begin{align*}
+%  n &= 6 \\
+%  \mu &= \frac{1595}{n} = 265.8 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(258 - 265.8)}^2 + {(273 - 265.8)}^2 + {(257 - 265.8)}^2 \\
+%    &+ {(264 - 265.8)}^2 + {(293 - 265.8)}^2 + {(250 - 265.8)}^2 \\
+%    &= 1182.8 \\
+%  \sigma^2 &= \frac{s}{n} = 197.1 \\
+%  \sigma &= \sqrt{\sigma^2} = 14.0
+%\end{align*}
+
+%\end{figure}
+%%}}}fig:ipPartC06
+
+%%{{{ fig:ipPartC10
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{ip_part_c10.png}
+%\caption{IP based partitioning for 10 crawlers}\label{fig:ipPartC10}
+%\begin{align*}
+%  n &= 10 \\
+%  \mu &= \frac{1595}{n} = 159.5 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(140 - 159.5)}^2 + {(175 - 159.5)}^2 + {(186 - 159.5)}^2 \\
+%    &+ {(166 - 159.5)}^2 + {(159 - 159.5)}^2 + {(152 - 159.5)}^2 \\
+%    &+ {(172 - 159.5)}^2 + {(148 - 159.5)}^2 + {(151 - 159.5)}^2 \\
+%    &+ {(146 - 159.5)}^2 \\
+%    &= 1964.5 \\
+%  \sigma^2 &= \frac{s}{n} = 196.4 \\
+%  \sigma &= \sqrt{\sigma^2} = 14.0
+%\end{align*}
+%\end{figure}
+%%}}}fig:ipPartC10
 
 \begin{table}[H]
   \centering
@@ -746,89 +772,116 @@ We will compare the variance \(\sigma^2\) and standard derivation \(\sigma\) to
   \num{6} & 265.8 & 197.1 & 14.0 & 5.3\% \\
   \num{10} & 159.5 & 196.4 & 14.0 & 8.8\% \\
 \end{tabular}
-  \caption{Variance and standard derivation for IP-based partitioning on \num{1595} IP addresses}\label{tab:varSmall}
+  \caption{Variance and standard deviation for IP-based partitioning on \num{1595} IP addresses}\label{tab:varSmall}
 \end{table}
 
-\Fref{tab:varSmall} shows that the derivation from the expected even distribution is within \SI{10}{\percent}.
+\Fref{tab:varSmall} shows that the deviation from the expected even distribution is within \SI{10}{\percent}.
-Since the used sample is not very big, according to the law of big numbers we would expect the derivation to get smaller, the bigger the sample gets.
+Since the used sample is not very big, according to the law of big numbers we would expect the deviation to get smaller, the bigger the sample gets.
 Therefore, we simulate the partitioning on a bigger sample of \num{1000000} random IP addresses.
 
-%{{{ fig:randIpPartC02
+\begin{landscape}
 \begin{figure}[H]
+\begin{subfigure}[b]{.6\textwidth}
 \centering
 \includegraphics[width=1\linewidth]{rand_ip_part_c02.png}
-\caption{IP based partitioning for 2 crawlers on generated dataset}\label{fig:randIpPartC02}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 2 \\
+\begin{subfigure}[b]{.6\textwidth}
-  \mu &= \frac{1000000}{n} = 500000 \\
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(499322 - 500000)}^2 + {(500678 - 500000)}^2 \\
-    &= 919368 \\
-  \sigma^2 &= \frac{s}{n} = 459684 \\
-  \sigma &= \sqrt{\sigma^2} = 678
-\end{align*}
-\end{figure}
-%}}}fig:randIpPartC02
-
-
-
-%{{{ fig:randIpPartC04
-\begin{figure}[H]
 \centering
 \includegraphics[width=1\linewidth]{rand_ip_part_c04.png}
-\caption{IP based partitioning for 4 crawlers on generated dataset}\label{fig:randIpPartC04}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 4 \\
+\hfill
-  \mu &= \frac{1000000}{n} = 250000 \\
+\begin{subfigure}[b]{.6\textwidth}
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(249504 - 250000)}^2 + {(250451 - 250000)}^2 + {(249818 - 250000)}^2 \\
-    &+ {(250227 - 250000)}^2 \\
-    &= 534070 \\
-  \sigma^2 &= \frac{s}{n} = 133517.5 \\
-  \sigma &= \sqrt{\sigma^2} = 365.4
-\end{align*}
-\end{figure}
-%}}}fig:randIpPartC04
-
-
-%{{{ fig:randIpPartC06
-\begin{figure}[H]
 \centering
 \includegraphics[width=1\linewidth]{rand_ip_part_c06.png}
-\caption{IP based partitioning for 6 crawlers on generated dataset}\label{fig:randIpPartC06}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 6 \\
+\begin{subfigure}[b]{.6\textwidth}
-  \mu &= \frac{1000000}{n} = 166666.7 \\
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(166430 - 166666.7)}^2 + {(166861 - 166666.7)}^2 + {(166269 - 166666.7)}^2 \\
-    &+ {(166937 - 166666.7)}^2 + {(166623 - 166666.7)}^2 + {(166880 - 166666.7)}^2 \\
-    &= 372413.3 \\
-  \sigma^2 &= \frac{s}{n} = 62068.9 \\
-  \sigma &= \sqrt{\sigma^2} = 249.1
-\end{align*}
-\end{figure}
-%}}}fig:randIpPartC06
-
-
-%{{{ fig:randIpPartC10
-\begin{figure}[H]
 \centering
 \includegraphics[width=1\linewidth]{rand_ip_part_c10.png}
-\caption{IP based partitioning for 10 crawlers on generated dataset}\label{fig:randIpPartC10}
+% \caption{IP based partitioning for 2 crawlers}\label{fig:ipPartC02}
-\begin{align*}
+\end{subfigure}%
-  n &= 10 \\
+\caption{IP based partitioning for crawlers on generated dataset}\label{fig:randIpPart}
-  \mu &= \frac{1000000}{n} = 100000 \\
-  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
-    &= {(100424 - 100000)}^2 + {(99650 - 100000)}^2 + {(99307 - 100000)}^2 \\
-    &+ {(100305 - 100000)}^2 + {(99403 - 100000)}^2 + {(100562 - 100000)}^2 \\
-    &+ {(100277 - 100000)}^2 + {(99875 - 100000)}^2 + {(99911 - 100000)}^2 \\
-    &+ {(100286 - 100000)}^2 \\
-    &= 1729874 \\
-  \sigma^2 &= \frac{s}{n} = 172987.4 \\
-  \sigma &= \sqrt{\sigma^2} = 415.9
-\end{align*}
 \end{figure}
-%}}}fig:randIpPartC10
+\end{landscape}
+
+%%{{{ fig:randIpPartC02
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{rand_ip_part_c02.png}
+%\caption{IP based partitioning for 2 crawlers on generated dataset}\label{fig:randIpPartC02}
+%\begin{align*}
+%  n &= 2 \\
+%  \mu &= \frac{1000000}{n} = 500000 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(499322 - 500000)}^2 + {(500678 - 500000)}^2 \\
+%    &= 919368 \\
+%  \sigma^2 &= \frac{s}{n} = 459684 \\
+%  \sigma &= \sqrt{\sigma^2} = 678
+%\end{align*}
+%\end{figure}
+%%}}}fig:randIpPartC02
+
+
+
+%%{{{ fig:randIpPartC04
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{rand_ip_part_c04.png}
+%\caption{IP based partitioning for 4 crawlers on generated dataset}\label{fig:randIpPartC04}
+%\begin{align*}
+%  n &= 4 \\
+%  \mu &= \frac{1000000}{n} = 250000 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(249504 - 250000)}^2 + {(250451 - 250000)}^2 + {(249818 - 250000)}^2 \\
+%    &+ {(250227 - 250000)}^2 \\
+%    &= 534070 \\
+%  \sigma^2 &= \frac{s}{n} = 133517.5 \\
+%  \sigma &= \sqrt{\sigma^2} = 365.4
+%\end{align*}
+%\end{figure}
+%%}}}fig:randIpPartC04
+
+
+%%{{{ fig:randIpPartC06
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{rand_ip_part_c06.png}
+%\caption{IP based partitioning for 6 crawlers on generated dataset}\label{fig:randIpPartC06}
+%\begin{align*}
+%  n &= 6 \\
+%  \mu &= \frac{1000000}{n} = 166666.7 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(166430 - 166666.7)}^2 + {(166861 - 166666.7)}^2 + {(166269 - 166666.7)}^2 \\
+%    &+ {(166937 - 166666.7)}^2 + {(166623 - 166666.7)}^2 + {(166880 - 166666.7)}^2 \\
+%    &= 372413.3 \\
+%  \sigma^2 &= \frac{s}{n} = 62068.9 \\
+%  \sigma &= \sqrt{\sigma^2} = 249.1
+%\end{align*}
+%\end{figure}
+%%}}}fig:randIpPartC06
+
+
+%%{{{ fig:randIpPartC10
+%\begin{figure}[H]
+%\centering
+%\includegraphics[width=1\linewidth]{rand_ip_part_c10.png}
+%\caption{IP based partitioning for 10 crawlers on generated dataset}\label{fig:randIpPartC10}
+%\begin{align*}
+%  n &= 10 \\
+%  \mu &= \frac{1000000}{n} = 100000 \\
+%  s &= \sum\limits_{i=1}^{n} {(x_i - \mu)}^2 \\
+%    &= {(100424 - 100000)}^2 + {(99650 - 100000)}^2 + {(99307 - 100000)}^2 \\
+%    &+ {(100305 - 100000)}^2 + {(99403 - 100000)}^2 + {(100562 - 100000)}^2 \\
+%    &+ {(100277 - 100000)}^2 + {(99875 - 100000)}^2 + {(99911 - 100000)}^2 \\
+%    &+ {(100286 - 100000)}^2 \\
+%    &= 1729874 \\
+%  \sigma^2 &= \frac{s}{n} = 172987.4 \\
+%  \sigma &= \sqrt{\sigma^2} = 415.9
+%\end{align*}
+%\end{figure}
+%%}}}fig:randIpPartC10
 
 
 \begin{table}[H]
@@ -840,11 +893,11 @@ Therefore, we simulate the partitioning on a bigger sample of \num{1000000} rand
   \num{6} & 166666.7 & 62069.9 & 249.1 & 0.15\% \\
   \num{10} & 100000.0 & 172987.4 & 415.9 & 0.42\% \\
 \end{tabular}
-  \caption{Variance and standard derivation for IP-based partitioning on \num{1000000} IP addresses}\label{tab:varBig}
+  \caption{Variance and standard deviation for IP-based partitioning on \num{1000000} IP addresses}\label{tab:varBig}
 \end{table}
 
 As expected, the work is still not perfectly distributed among the crawlers but evenly enough for our use case.
-The derivation for larger botnets is within \SI{0.5}{\percent} of the even distribution.
+The deviation for larger botnets is expected to be within \SI{0.5}{\percent} of the even distribution.
 This is good enough for balancing the tasks among workers.
 
 %}}} eval load balancing
@@ -853,7 +906,7 @@ This is good enough for balancing the tasks among workers.
 \subsection{Reduction of Request Frequency}
 
 To evaluate the request frequency optimization described in \Fref{sec:stratRedReqFreq}, crawl a simulated peer and check if the requests are evenly distributed and how big the deviation from the theoretically optimal result is.
-To get more realistic results, the crawlers and simulated peer are running on different machines so they are not within the same LAN.
+To get more realistic results, the crawlers and simulated peer are running on different machines so they are not within the same LAN\@.
 We use the same parameters as in the example above:
 
 \begin{align*}
@@ -899,17 +952,64 @@ To recap, this is what the optimal timeline would look like:
 \end{center}
 
 
-The ideal distribution would be \SI{2.5}{\second} between each two events.
+The ideal distribution would be \SI{2.5}{\second} between every two events.
 Due to network latency and load from crawling other peers, we expect the actual result to deviate from the optimal value over time.
-With this experiment we try to estimate the impact of the latency.
+With this experiment, we try to estimate the impact of the latency.
-If it is existent and measurable the crawlers have to be rescheduled periodically to keep the deviation at an acceptable level.
 
 \begin{figure}[H]
   \centering
-  \includegraphics[width=1\linewidth]{time_deriv.png}
+  \includegraphics[width=1\linewidth]{time_devi.png}
-  \caption{Derivation from the expected interval}\label{fig:timeDeriv}
+  \caption{Deviation from the expected interval}\label{fig:timeDevi}
 \end{figure}
 
+\begin{landscape}
+\begin{figure}[H]
+  \centering
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{time_deviation/time_devi_c0.png}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{time_deviation/time_devi_c1.png}
+\end{subfigure}%
+\hfill
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{time_deviation/time_devi_c2.png}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{time_deviation/time_devi_c3.png}
+\end{subfigure}%
+  \caption{Derivation from the expected interval per crawler}\label{fig:perCralwerDeviation}
+\end{figure}
+\end{landscape}
+
+The deviation between crawl events per crawler is below \SI{0.01}{\second} most of the time, with occasional outliers due to network latency or server load.
+
+\begin{table}[H]
+\centering
+\begin{tabular}{rr}
+  \textbf{Crawler} & \textbf{Average Deviation} \\
+  c0 & \num{0.0005927812081134085} \\
+  c1 & \num{0.0003700713297978895} \\
+  c2 & \num{0.0006121075253902246} \\
+  c3 & \num{0.0020807891511268814} \\
+\end{tabular}
+  \caption{Average deviation per crawler}\label{tab:perCralwerDeviation}
+\end{table}
+
+The average deviation per crawler is below \SI{0.002}{\second} even with some huge outliers. In general it is below \SI{0.0007}{\second}, which is a surprisingly accurate result.
+In real-world scenarios, crawlers will monitor more than a single peer and the scheduling is expected to be less accurate.
+Still, the deviation will always stay below the effective frequency \(f\), because after exceeding \(f\), a crawler is overtaken by the next in line.
+The impact of the deviation when crawling real-world botnets has to be investigated and if it shows to be a problem, the tasks have to be rescheduled periodically to prevent this from happening.
+
+% \begin{figure}[H]
+%   \centering
+%   \includegraphics[width=1\linewidth]{time_devi_overtake.png}
+% \end{figure}
+
 
 %}}} eval redu requ freq
 
@@ -920,8 +1020,8 @@ If it is existent and measurable the crawlers have to be rescheduled periodicall
 \subsubsection{Use Other Known Sensors}
 
 By connecting the known sensors and effectively building a complete graph \(K_{\abs{C}}\) between them creates \(\abs{C} - 1\) outgoing edges per sensor.
-In most cases this won't be enough to reach the amount of edges that would be needed.
+In most cases, this won't be enough to reach the number of edges that would be needed.
-Also this does not help against the \ac{wcc} metric since this would create a bigger but still disconnected component.
+Also, this does not help against the \ac{wcc} metric since this would create a bigger but still disconnected component.
 
 % TODO: caption, label
 \begin{figure}[H]
@@ -988,7 +1088,7 @@ For the \ac{wcc} metric, it is obvious that even a single edge back into the mai
 
 \subsubsection{Effectiveness against Page- and SensorRank}
 
-In this section we will evaluate how adding outgoing edges to a sensor impacts it's PageRank and SensorRank values.
+In this section, we will evaluate how adding outgoing edges to a sensor impacts its PageRank and SensorRank values.
 Before doing so, we will check the impact of the initial rank by calculating it with different initial values and comparing the value distribution of the result.
 
 \begin{table}[H]
@@ -1075,173 +1175,102 @@ Before doing so, we will check the impact of the initial rank by calculating it
   \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}_0(v) = 0.75\)}\label{fig:dist_sr_75}
 \end{figure}
 
-The distribution graphs in \Fref{fig:dist_sr_25}, \Fref{fig:dist_sr_50} and \Fref{fig:dist_sr_75} show that the initial rank has no effect on the distribution, only on the actual numeric rank values and how far apart they are spread.
+The distribution graphs in \Fref{fig:dist_sr_25}, \Fref{fig:dist_sr_50}, and \Fref{fig:dist_sr_75} show that the initial rank has no effect on the distribution, only on the actual numeric rank values and how far apart they are spread.
 
-For all combinations of initial value and PageRank iterations, the rank for a well-known crawler is in the \nth{95} percentile, so for our use case---detecting sensors due their high ranks---those parameters do not matter.
+For all combinations of initial value and PageRank iterations, the rank for a well-known crawler is in the \nth{95} percentile, so for our use case---detecting sensors due to their high ranks---those parameters do not matter.
 
 % On average, peers in the analyzed dataset have \num{223} successors over the whole week.
 Looking at the data in smaller buckets of one hour each, the average number of successors per peer is \num{90}.
 
-%{{{ fig:avg_out_edges
+%%{{{ fig:avg_out_edges
-\begin{figure}[H]
+%\begin{figure}[H]
-\centering
+%\centering
-\includegraphics[width=1\linewidth]{./avg_out_edges.png}
+%\includegraphics[width=1\linewidth]{./avg_out_edges.png}
-\caption{Average outgoing edges per peer per hour}\label{fig:avg_out_edges}
+%\caption{Average outgoing edges per peer per hour}\label{fig:avg_out_edges}
-\end{figure}
+%\end{figure}
-%}}}fig:avg_out_edges
+%%}}}fig:avg_out_edges
 
 Experiments were performed, in which the incoming edges for the known sensor are reduced by increasing factors, to see, when the sensor's rank reaches the overall average.
 
-% \begin{figure}[H]
+\begin{landscape}
-%   \centering
-%   \includegraphics[width=1\textwidth]{reduced_ranks/0/in_out.png}
-%   % \caption{PageRank after adding \(0.75 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr75}
-% \end{figure}%
-
 \begin{figure}[H]
   \centering
-\begin{subfigure}[b]{1\textwidth}
+\begin{subfigure}[b]{.6\textwidth}
   \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/0/pr.png}
+  \includegraphics[width=1\linewidth]{reduced_ranks/0/in_out.png}
-  \caption{PageRank after removing \SI{0}{\percent} of edges}\label{fig:pr0}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/1/in_out.png}
 \end{subfigure}%
 \hfill
-\begin{subfigure}[b]{1\textwidth}
+\begin{subfigure}[b]{.6\textwidth}
   \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/0/sr.png}
+  \includegraphics[width=1\linewidth]{reduced_ranks/2/in_out.png}
-  \caption{SensorRank after removing \SI{0}{\percent} of edges}\label{fig:sr0}
 \end{subfigure}%
-  % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/3/in_out.png}
+\end{subfigure}%
+  \caption{In-degrees after removing edges}\label{fig:prFiltered}
 \end{figure}
+\end{landscape}
+
+\begin{landscape}
+\begin{figure}[H]
+  \centering
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/0/pr.png}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/1/pr.png}
+\end{subfigure}%
+\hfill
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/2/pr.png}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/3/pr.png}
+\end{subfigure}%
+  \caption{PageRank after removing edges}\label{fig:prFiltered}
+\end{figure}
+\end{landscape}
+
+\begin{landscape}
+\begin{figure}[H]
+  \centering
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/0/sr.png}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/1/sr.png}
+\end{subfigure}%
+\hfill
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/2/sr.png}
+\end{subfigure}%
+\begin{subfigure}[b]{.6\textwidth}
+  \centering
+  \includegraphics[width=1\linewidth]{reduced_ranks/3/sr.png}
+\end{subfigure}%
+  \caption{SensorRank after removing edges}\label{fig:srFiltered}
+\end{figure}
+\end{landscape}
 
 \Fref{fig:pr0} and \Fref{fig:sr0} show the situation on the base truth without modifications.
 
-\begin{figure}[H]
-  \centering
-  \includegraphics[width=.8\textwidth]{reduced_ranks/1/in_out.png}
-  \caption{Incoming edges after removing \SI{10}{\percent} of edges}\label{fig:in1}
-  % \caption{PageRank after adding \(0.75 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr75}
-\end{figure}%
 
-\begin{figure}[H]
+We can see in \Fref{fig:prFiltered} and \Fref{fig:srFiltered}, that we have to reduce the incoming edges by \SI{20}{\percent} and \SI{30}{\percent} respectively to get average values for SensorRank and PageRank.
-  \centering
+This also means that the number of incoming edges for a sensor must be about the same as the average about of incoming edges as can be seen in \Fref{fig:in3}.
-\begin{subfigure}[b]{1\textwidth}
+Depending on the protocol details of the botnet (\eg{} how many incoming edges are allowed per peer), this means that a large amount of sensors is needed if we want to monitor the whole network.
-  \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/1/pr.png}
-  \caption{PageRank after removing \SI{10}{\percent} of edges}\label{fig:pr1}
-\end{subfigure}%
-\hfill
-\begin{subfigure}[b]{1\textwidth}
-  \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/1/sr.png}
-  \caption{SensorRank after removing \SI{10}{\percent} of edges}\label{fig:sr1}
-\end{subfigure}%
-  % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
-\end{figure}
 
-\begin{figure}[H]
-  \centering
-  \includegraphics[width=.8\textwidth]{reduced_ranks/2/in_out.png}
-  \caption{Incoming edges after removing \SI{20}{\percent} of edges}\label{fig:in2}
-  % \caption{PageRank after adding \(0.75 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr75}
-\end{figure}%
-
-\begin{figure}[H]
-  \centering
-\begin{subfigure}[b]{1\textwidth}
-  \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/2/pr.png}
-  \caption{PageRank after removing \SI{20}{\percent} of edges}\label{fig:pr2}
-\end{subfigure}%
-\hfill
-\begin{subfigure}[b]{1\textwidth}
-  \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/2/sr.png}
-  \caption{SensorRank after removing \SI{20}{\percent} of edges}\label{fig:sr2}
-\end{subfigure}%
-  % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
-\end{figure}
-
-\begin{figure}[H]
-  \centering
-  \includegraphics[width=.8\textwidth]{reduced_ranks/3/in_out.png}
-  \caption{Incoming edges after removing \SI{30}{\percent} of edges}\label{fig:in3}
-  % \caption{PageRank after adding \(0.75 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr75}
-\end{figure}%
-
-\begin{figure}[H]
-  \centering
-\begin{subfigure}[b]{1\textwidth}
-  \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/3/pr.png}
-  \caption{PageRank after removing \SI{30}{\percent} of edges}\label{fig:pr3}
-\end{subfigure}%
-\hfill
-\begin{subfigure}[b]{1\textwidth}
-  \centering
-  \includegraphics[width=.8\linewidth]{reduced_ranks/3/sr.png}
-  \caption{SensorRank after removing \SI{30}{\percent} of edges}\label{fig:sr3}
-\end{subfigure}%
-  % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
-\end{figure}
-
-We can see in \Fref{fig:sr2} and \Fref{fig:pr3}, that we have to reduce the incoming edges by \SI{20}{\percent} and \SI{30}{\percent} respectively to get average values for SensorRank and PageRank.
-This also means, that the amount of incoming edges for a sensor must be about the same as the average about of incoming edges as can be seen in \Fref{fig:in3}.
-Depending on the protocol details of the botnet (\eg{} how many incoming edges are allowed per peer), this means that a large amount of sensors is needed, if we want to monitor the whole network.
-
-% Experiments were performed, in which a percentage of random outgoing edges were added to the known sensor, based on the amount of incoming edges:
-% We evaluate the impact of outgoing edges by picking a percentage of random nodes in each bucket and creating edges from the sensor to each of the sampled peers, thereby evening the ratio between \(\deg^{+}\) and \(\deg^{-}\).
-
-
-% \begin{figure}[H]
-%   \centering
-% \begin{subfigure}[b]{.75\textwidth}
-%   \centering
-%   \includegraphics[width=1\linewidth]{ranks/pr_75.png}
-%   \caption{PageRank after adding \(0.75 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr75}
-% \end{subfigure}%
-% \hfill
-% \begin{subfigure}[b]{.75\textwidth}
-%   \centering
-%   \includegraphics[width=1\linewidth]{ranks/sr_75.png}
-%   \caption{SensorRank after adding \(0.75 \times \abs{\text{pred}(v)}\) edges}\label{fig:sr75}
-% \end{subfigure}%
-%   % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
-% \end{figure}
-
-% \begin{figure}[H]
-%   \centering
-% \begin{subfigure}[b]{.75\textwidth}
-%   \centering
-%   \includegraphics[width=1\linewidth]{ranks/pr_100.png}
-%   \caption{PageRank after adding \(1.0 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr100}
-% \end{subfigure}%
-% \hfill
-% \begin{subfigure}[b]{.75\textwidth}
-%   \centering
-%   \includegraphics[width=1\linewidth]{ranks/sr_100.png}
-%   \caption{SensorRank after adding \(1.0 \times \abs{\text{pred}(v)}\) edges}\label{fig:sr100}
-% \end{subfigure}%
-%   % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
-% \end{figure}
-
-% \begin{figure}[H]
-%   \centering
-% \begin{subfigure}[b]{.75\textwidth}
-%   \centering
-%   \includegraphics[width=1\linewidth]{ranks/pr_150.png}
-%   \caption{PageRank after adding \(1.5 \times \abs{\text{pred}(v)}\) edges}\label{fig:pr150}
-% \end{subfigure}%
-% \hfill
-% \begin{subfigure}[b]{.75\textwidth}
-%   \centering
-%   \includegraphics[width=1\linewidth]{ranks/sr_150.png}
-%   \caption{SensorRank after adding \(1.5 \times \abs{\text{pred}(v)}\) edges}\label{fig:sr150}
-% \end{subfigure}%
-%   % \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75}
-% \end{figure}
-
-% These results show, that simply adding new edges is not enough and we need to limit the incoming edges to improve the Page- and SensorRank metrics.
 
 %}}} eval creating edges
 
@@ -1317,7 +1346,7 @@ We were able to show, that a collaborative monitoring approach for \ac{p2p} botn
 On the other hand, graph ranking algorithms have been proven to be hard to bypass without requiring large amounts of sensor nodes.
 
 Luckily most of the anti-monitoring and monitoring detection techniques discussed in this work are of academic nature and have not yet been deployed in real-world botnets.
-Further investigation and improvements in \ac{p2p} botnet monitoring are required to prevent a situation were a botmaster implements the currently theoretical concepts and renders monitoring as it is currently done, ineffective.
+Further investigation and improvements in \ac{p2p} botnet monitoring are required to prevent a situation where a botmaster implements the currently theoretical concepts and renders monitoring as it is currently done, ineffective.
 
 %}}} conclusion
 

report.tex

@@ -33,6 +33,8 @@ headsepline,
 \usepackage{mathtools}
 
 % \usepackage{tikz}
+% figures in landscape mode
+\usepackage{lscape}
 
 % positioning
 \usepackage{float}