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\ No newline at end of file diff --git a/content.tex b/content.tex index 9bc55222..c5c80782 100644 --- a/content.tex +++ b/content.tex @@ -605,7 +605,6 @@ The following candidates to place on the neighbor list will be investigated: Returning all the other sensors when responding to peer list requests, thereby effectively creating a complete graph \(K_\abs{C}\) among the workers, creates valid outgoing edges. The resulting graph will still form a \ac{wcc} with now edges back into the main network. -Also, this would leak the information about all known sensors to the botmasters. %{{{ churned peers \subsubsection{Churned Peers After IP Rotation} @@ -634,7 +633,7 @@ Those peers can be used as fake neighbors and create valid-looking outgoing edge \clearpage{} \section{Evaluation} -To evaluate the strategies from above, we took a snapshot of the Sality~\cite{bib:falliere_sality_2011} botnet obtained from \ac{bms} throughout of \daterange{2021-04-21}{2021-04-28}. +To evaluate the strategies from above, we took a snapshot of the Sality~\cite{bib:falliere_sality_2011} botnet obtained from \ac{bms} throughout of \daterange{2021-04-21}{2021-04-28}, if not stated otherwise. %{{{ eval load balancing \subsection{Load Balancing} @@ -875,21 +874,53 @@ Applying PageRank with an initial rank of \(0.25\) once on the example graphs in While this works for small networks, the crawlers must account for a significant amount of peers in the network for this change to be noticeable. The generated \(K_n\) needs to be at least as big as the smallest regular component in the botnet, which is not feasible. +Also, if detected, this would leak the information about all known sensors to the botmasters. +The limited scalability, and potential information leak, which might be used by botmasters to retaliate against the sensors or the whole monitoring operation, make this approach unusable in real-world scenarios. %}}} other sensors + +\subsubsection{Effectiveness against SensorBuster} + +SensorBuster relies on the assumption that sensors don't have any outgoing edges, thereby creating a disconnected graph component. + +\begin{figure}[H] +\centering +\begin{subfigure}[b]{.5\textwidth} + \centering + \includegraphics[width=.8\linewidth]{sensorbuster/sensor_without_outgoing.drawio.pdf} + \caption{Sensor without outgoing edge creates disconnected graph component} +\end{subfigure}% +\begin{subfigure}[b]{.5\textwidth} + \centering + \includegraphics[width=.8\linewidth]{sensorbuster/sensor_with_outgoing.drawio.pdf} + \caption{Single outgoing edge connects sensor back to the main component}\label{fig:sensorbusterWithOutgoing} +\end{subfigure}% +\end{figure} + +\Fref{fig:sensorbusterWithOutgoing} shows how a single valid edge back into the network (from \emph{Sensor} to peer \num{3} in the example) renders the SensorBuster metric ineffective by making the sensor part of the main graph component. + +For the \ac{wcc} metric, it is obvious that even a single edge back into the main network is enough to connect the sensor back to the main graph and therefore beat this metric. + +\todo{formulieren} + +\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. +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] \centering \begin{tabular}{lllll} \textbf{Iteration} & \textbf{Avg. PR} & \textbf{Crawler PR} & \textbf{Avg. SR} & \textbf{Crawler SR} \\ - 1 & 0.24854932 & 0.63277194 & 0.15393478 & 0.56545578 \\ - 2 & 0.24854932 & 0.63277194 & 0.15393478 & 0.56545578 \\ - 3 & 0.24501068 & 0.46486353 & 0.13810930 & 0.41540997 \\ - 4 & 0.24501068 & 0.46486353 & 0.13810930 & 0.41540997 \\ - 5 & 0.24233737 & 0.50602884 & 0.14101354 & 0.45219598 \\ + \num{1} & \num{0.24854932} & \num{0.63277194} & \num{0.15393478} & \num{0.56545578} \\ + \num{2} & \num{0.24854932} & \num{0.63277194} & \num{0.15393478} & \num{0.56545578} \\ + \num{3} & \num{0.24501068} & \num{0.46486353} & \num{0.13810930} & \num{0.41540997} \\ + \num{4} & \num{0.24501068} & \num{0.46486353} & \num{0.13810930} & \num{0.41540997} \\ + \num{5} & \num{0.24233737} & \num{0.50602884} & \num{0.14101354} & \num{0.45219598} \\ \end{tabular} - \caption{Values for PageRank iterations with initial rank \(\forall v \in V : \text{PR}(v) = 0.25\)}\label{tab:pr_iter_table_25} + \caption{Values for PageRank iterations with initial rank \(\forall v \in V : \text{PR}_0(v) = 0.25\)}\label{tab:pr_iter_table_25} \end{table} \begin{figure}[H] @@ -904,20 +935,20 @@ The generated \(K_n\) needs to be at least as big as the smallest regular compon \includegraphics[width=1\linewidth]{0.25_5_sr.png} \caption{Distribution after 5 iterations}\label{fig:dist_sr_25_5} \end{subfigure}% - \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.25\)}\label{fig:dist_sr_25} + \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}_0(v) = 0.25\)}\label{fig:dist_sr_25} \end{figure} \begin{table}[H] \centering \begin{tabular}{lllll} \textbf{Iteration} & \textbf{Avg. PR} & \textbf{Crawler PR} & \textbf{Avg. SR} & \textbf{Crawler SR} \\ - 1 & 0.49709865 & 1.26554389 & 0.30786955 & 1.13091156 \\ - 2 & 0.49709865 & 1.26554389 & 0.30786955 & 1.13091156 \\ - 3 & 0.49002136 & 0.92972707 & 0.27621861 & 0.83081993 \\ - 4 & 0.49002136 & 0.92972707 & 0.27621861 & 0.83081993 \\ - 5 & 0.48467474 & 1.01205767 & 0.28202708 & 0.90439196 \\ + \num{1} & \num{0.49709865} & \num{1.26554389} & \num{0.30786955} & \num{1.13091156} \\ + \num{2} & \num{0.49709865} & \num{1.26554389} & \num{0.30786955} & \num{1.13091156} \\ + \num{3} & \num{0.49002136} & \num{0.92972707} & \num{0.27621861} & \num{0.83081993} \\ + \num{4} & \num{0.49002136} & \num{0.92972707} & \num{0.27621861} & \num{0.83081993} \\ + \num{5} & \num{0.48467474} & \num{1.01205767} & \num{0.28202708} & \num{0.90439196} \\ \end{tabular} - \caption{Values for PageRank iterations with initial rank \(\forall v \in V : \text{PR}(v) = 0.5\)}\label{tab:pr_iter_table_5} + \caption{Values for PageRank iterations with initial rank \(\forall v \in V : \text{PR}_0(v) = 0.5\)}\label{tab:pr_iter_table_5} \end{table} \begin{figure}[H] @@ -932,20 +963,20 @@ The generated \(K_n\) needs to be at least as big as the smallest regular compon \includegraphics[width=1\linewidth]{0.50_5_sr.png} \caption{Distribution after 5 iterations}\label{fig:dist_sr_50_5} \end{subfigure}% - \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.5\)}\label{fig:dist_sr_50} + \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}_0(v) = 0.5\)}\label{fig:dist_sr_50} \end{figure} \begin{table}[H] \centering \begin{tabular}{lllll} \textbf{Iteration} & \textbf{Avg. PR} & \textbf{Crawler PR} & \textbf{Avg. SR} & \textbf{Crawler SR} \\ - 1 & 0.74564797 & 1.89831583 & 0.46180433 & 1.69636734 \\ - 2 & 0.74564797 & 1.89831583 & 0.46180433 & 1.69636734 \\ - 3 & 0.73503203 & 1.39459060 & 0.41432791 & 1.24622990 \\ - 4 & 0.73503203 & 1.39459060 & 0.41432791 & 1.24622990 \\ - 5 & 0.72701212 & 1.51808651 & 0.42304062 & 1.35658794 \\ + \num{1} & \num{0.74564797} & \num{1.89831583} & \num{0.46180433} & \num{1.69636734} \\ + \num{2} & \num{0.74564797} & \num{1.89831583} & \num{0.46180433} & \num{1.69636734} \\ + \num{3} & \num{0.73503203} & \num{1.39459060} & \num{0.41432791} & \num{1.24622990} \\ + \num{4} & \num{0.73503203} & \num{1.39459060} & \num{0.41432791} & \num{1.24622990} \\ + \num{5} & \num{0.72701212} & \num{1.51808651} & \num{0.42304062} & \num{1.35658794} \\ \end{tabular} - \caption{Values for PageRank iterations with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{tab:pr_iter_table_75} + \caption{Values for PageRank iterations with initial rank \(\forall v \in V : \text{PR}_0(v) = 0.75\)}\label{tab:pr_iter_table_75} \end{table} \begin{figure}[H] @@ -960,26 +991,27 @@ The generated \(K_n\) needs to be at least as big as the smallest regular compon \includegraphics[width=1\linewidth]{0.75_5_sr.png} \caption{Distribution after 5 iterations}\label{fig:dist_sr_75_5} \end{subfigure}% - \caption{SensorRank distribution with initial rank \(\forall v \in V : \text{PR}(v) = 0.75\)}\label{fig:dist_sr_75} + \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. +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, 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 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}.\todo{timeline with peers per bucket} +% 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 -\begin{figure}[h] +\begin{figure}[H] \centering \includegraphics[width=1\linewidth]{./avg_out_edges.png} \caption{Average outgoing edges per peer per hour}\label{fig:avg_out_edges} \end{figure} -\todo{use better data?} %}}}fig:avg_out_edges -Experiments were performed, in which a percentage of random outgoing edges were added to the known sensor, based on the amount of incoming edges: +% 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 @@ -1030,7 +1062,6 @@ Experiments were performed, in which a percentage of random outgoing edges were \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. -For the \ac{wcc} metric, it is obvious that 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