We use the internet to communicate, shop, handle financial transactions, and much more.
Many personal and professional workflows are so dependent on the internet, that they won't work when being offline, and with the pandemic, we are living through, this dependency grew even bigger.
The number of connected \ac{iot} devices is around 10 billion in 2021 and is estimated to be constantly growing over the next years up to 25 billion in 2030~\cite{bib:statista_iot_2020}.
Many of these devices run on outdated software, don't receive any updates, and don't follow general security best practices.
While in 2016 only \SI{77}{\percent} of German households had a broadband connection with a bandwidth of \SI{50}{\mega\bit\per\second} or more, in 2020 it was already \SI{95}{\percent} with more than \SI{50}{\mega\bit\per\second} and \SI{59}{\percent} with at least \SI{1000}{\mega\bit\per\second}~\cite{bib:statista_broadband_2021}.
Their nature as small devices---often without any direct user interaction---that are always online and behind internet connections that are getting faster and faster makes them a desirable target for botnets.
In recent years, \ac{iot} botnets have been responsible for some of the biggest \ac{ddos} attacks ever recorded---creating up to \SI{1}{\tera\bit\per\second} of traffic~\cite{bib:ars_ddos_2016}.
A botnet is a network of infected computers with some means of communication to control the infected systems.
Classic botnets use one or more central coordinating hosts called \ac{c2} servers.
These \ac{c2} servers could use any protocol from \ac{irc} over \ac{http} to Twitter~\cite{bib:pantic_covert_2015} as communication channel with the infected hosts.
Abusive use of infected systems includes several things\todo{things = bad}---\ac{ddos} attacks, banking fraud, as proxies to hide the attacker's identity, send spam emails\dots{}
Analyzing and shutting down a centralized botnet is comparatively easy since the central means of communication (the \ac{c2} IP address or domain name, Twitter handle or \ac{irc} channel) are publicly known.
A coordinated operation with help from law enforcement, hosting providers, domain registrars, and platform providers could shut down or take over the operation by changing how requests are routed or simply shutting down the controlling servers/accounts.
To complicate 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 \ac{dns}, where a large pool of IP addresses is used assigned randomly to the \ac{c2} domains to prevent IP based blacklisting~\cite{bib:nazario_as_2008}.
A number of botnet operations were shut down like this~\cite{bib:nadji_beheading_2013} and as the defenders upped their game, so did attackers---the concept of \ac{p2p} botnets emerged.
The idea is to build a decentralized network without \acp{spof} in the form of \ac{c2} servers as shown in \autoref{fig:p2p}.
In a \ac{p2p} botnet, each node in the network knows a number of its neighbors and connects to those, each of these neighbors has a list of neighbors on his own, and so on.
Any of the nodes in \autoref{fig:p2p} could be the bot master but they don't even have to be online all the time since the peers will stay connected autonomously.
This lack of a \ac{spof} makes \ac{p2p} botnets more resilient to take-down attempts since the communication is not stopped and bot masters can easily rejoin the network and send commands.
The constantly growing damage produced by botnets has many researchers and law enforcement agencies trying to shut down these operations~\cite{bib:nadji_beheading_2013, bib:nadji_still_2017, bib:dittrich_takeover_2012, bib:fbiTakedown2014}.
The monetary value of these botnets directly correlates with the amount of effort bot masters are willing to put into implementing defense mechanisms against take-down attempts.
Some of these countermeasures include deterrence, which limits the number of allowed bots per IP address or subnet to 1; blacklisting, where known crawlers and sensors are blocked from communicating with other bots in the network (mostly IP based); disinformation, when fake bots are placed in the neighborhood lists, which invalidates the data collected by crawlers; and active retaliation like \ac{ddos} attacks against sensors or crawlers~\cite{bib:andriesse_reliable_2015}.
This has some advantages in that it is not possible for bot masters to detect or prevent data collection of that kind, but it is not trivial to distinguish valid \ac{p2p} application traffic (\eg{} BitTorrent, Skype, cryptocurrencies, \ldots) from \ac{p2p} bots.
As most detection botnet mechanisms, also the passive ones work by building communication graphs and finding tightly coupled subgraphs that might be indicative of a botnet~\cite{bib:botgrep2010}. An advantage of passive detection is, that it is independent of protocol details, specific binaries or the structure of the network (\ac{p2p} vs.\ centralized)~\cite{bib:botminer2008}.
\item Large scale network analysis (hard to differentiate from legitimate \ac{p2p} traffic (\eg{} BitTorrent), hard to get data, knowledge of some known bots required)~\cite{bib:zhang_building_2014}
In this case, a subset of the botnet protocol are reimplemented to place pseudo-bots or sensors in the network, which will only communicate with other nodes but won't accept or execute commands to perform malicious actions.
The difference in behaviour from the reference implementation and conspicuous graph properties (\eg{} high \(\deg^{+}\) vs.\ low \(\deg^{-}\)) of these sensors allows bot masters to detect and block the sensor nodes.
The implementation of the concepts of this work will be done as part of \ac{bms}\footnotemark, a monitoring platform for \ac{p2p} botnets described by \citeauthor{bib:bock_poster_2019} in~\cite{bib:bock_poster_2019}.
\Ac{bms} uses a hybrid active approach of crawlers and sensors (reimplementations of the \ac{p2p} protocol of a botnet, that won't perform malicious actions) to collect live data from active botnets.
In an earlier project, I implemented different node ranking algorithms (among others \enquote{PageRank}~\cite{bib:page_pagerank_1998}) to detect sensors and crawlers in a botnet, as described in \citetitle{bib:karuppayah_sensorbuster_2017}.
The goal of this work is to complicate detection mechanisms like this for bot masters by centralizing the coordination of the system's crawlers and sensors, thereby reducing the node's rank for specific graph metrics.
The coordinated work distribution also helps in efficiently monitoring large botnets where one sensor is not enough to track all peers.
The final results should be as general as possible and not depend on any botnet's specific behaviour, but it assumes, that every \ac{p2p} botnet has some kind of \enquote{getNeighbourList} method in the protocol, that allows other peers to request a list of active nodes to connect to.
In the current implementation, each crawler will itself visit and monitor each new node it finds.
The idea for this work is to report newfound nodes back to the \ac{bms} backend first, where the graph of the known network is created, and a fitting worker is selected to archive the goal of the according coordination strategy.
If it is not possible, to select a specific sensor so that the monitoring activity stays inconspicuous, the coordinator can do a complete shuffle of all nodes between the sensors to restore the wanted graph properties or warn if more sensors are required to stay undetected.
The improved crawler system should allow new crawlers to register themselves and their capabilities (\eg{} bandwidth, geolocation ), so the amount of work can be scaled accordingly between hosts.
If time allows, \ac{bsf}\footnotemark{} will be used to simulate a botnet place sensors in the simulated network and measure the improvement achieved by the coordinated monitoring effort.
\mintinline{go}{register(capabilities)}: Register new worker with capabilities (which botnet, available bandwidth, \ldots). This is called periodically and used to determine which worker is still active, when splitting the workload.
\mintinline{go}{reportPeer(peers)}: Report found targets. Both successful and failed attempts are reported, to detect as soon as possible, when a peer became unavailable.
\mintinline{go}{reportEdge(edges)}: Report found edges. Edges are found by querying the neighbourhood list of known peers. This is how new peers are detected.
\mintinline{go}{requestTasks() []PeerTask}: Receive a batch of crawl tasks from the coordinator. The tasks consist of the target peer, if the crawler should start or stop the operation, when it should start and stop monitoring and the frequency.
This assumption greatly simplifies the implementation due to the lack of changing state that has to be tracked while still exploring the described strategies.
A production-ready implementation of the described techniques can drop this assumption but might have to recalculate the work distribution once a crawler joins or leaves.
\item Assuming IP addresses are evenly distributed and so are infections, take the IP address as an \SI{32}{\bit} integer modulo \(\abs{C}\). See~\autoref{sec:ip_part}
Load balancing in itself does not help prevent the detection of crawlers but it allows better usage of available resources.
No peer will be crawled by more than one crawler and it allows crawling of bigger botnets where the current approach would reach its limit and could also be worked around with scaling up the machine where the crawler is executed.
Load balancing allows scaling out, which can be more cost-effective.
For the sake of simplicity, only the bandwidth will be considered as capability but it can be extended by any shared property between the crawlers, \eg{} available memory, CPU speed.
For a given crawler \(c_i \in C\) let \(B(c_i)\) be the total bandwidth of the crawler.
The total available bandwidth is \(b =\sum\limits_{c \in C} B(c_i)\).
The weight \(W(c_i)=\frac{B}{B(c_i)}\)\todo{proper def for weight} defines which percentage of the work gets assigned to \(c_i\).
The set of target peers \(P = <p_0, p_1, \ldots, p_{n-1}>\), is partitioned into \(|C|\) subsets according to \(W(c_i)\) and each subset is assigned to its crawler \(c_i\).
The mapping \mintinline{go}{gcd(C)} is the greatest common divisor of all peers in \mintinline{go}{C}, \(\text{maxWeight}(C)=\max\{\forall c \in C : W(c)\}\).
This creates a list of crawlers where a crawler can occur more than once, depending on its capabilities.
The set of crawlers \(\{a, b, c\}\) with capabilities \(cap(a)=3, cap(b)=2, cap(c)=1\) would produce \(<a, b, c, a, b, a>\), allocating two and three times the work to crawlers \(b\) and \(a\) respectively.
The output of cryptographic hash functions is uniformly distributed---even substrings of the calculated hash hold this property.
Calculating the hash of an IP address and distributing the work with regard to \(\text{hash}(\text{IP})\mod\abs{C}\) creates about evenly sized buckets for each worker to handle.
This gives us the mapping \(m(i)=\text{hash}(i)\mod\abs{C}\) to sort peers into buckets.
Any hash function can be used but since it must be calculated often, a fast function should be used.
While the \ac{md5} hash function must be considered broken for cryptographic use, it is faster to calculate than hash functions with longer output.
For the use case at hand, only the uniform distribution property is required so \ac{md5} can be used without scarifying any kind of security.
This strategy can also be weighted using the crawlers capabilities by modifying the list of available workers so that a worker can appear multiple times according to its weight.
\caption{Distribution of the lowest byte of \ac{md5} hashes over IPv4}\label{fig:md5IPDist}
\end{figure}
\ac{md5} returns a \SI{128}{\bit} hash but Go cannot directly work with \SI{128}{\bit} integers.
It would be possible to implement the modulo operation for arbitrarily sized integers, but the uniform distribution also holds substrings of hashes.
\autoref{fig:md5IPDist} shows the distribution of the lowest \SI{8}{\bit} for \ac{md5} hashes over all \(2^{32}\) IP addresses in their representation as \SI{32}{\bit} integers.
By exploiting the even distribution offered by hashing, the work of each crawler is also evenly distributed over all IP subnets, \ac{as} and geolocations.
This ensures neighboring peers (\eg{} in the same \ac{as}, geolocation or IP subnet) get visited by different crawlers.
Using collaborative crawlers, an arbitrarily fast frequency can be achieved without being blacklisted.
With \(L \in\mathbb{N}\) being the frequency limit at which a crawler will be blacklisted, \(F \in\mathbb{N}\) being the crawl frequency that should be achieved.
The amount of crawlers \(C\) required to achieve the frequency \(F\) without being blacklisted and the offset \(O\) between crawlers are defined as
Taking advantage of the \mintinline{go}{StartAt} field from the \mintinline{go}{PeerTask} returned by the \mintinline{go}{requestTasks} primitive above, the crawlers can be scheduled offset by \(O\) at a frequency \(L\) to ensure, the overall requests to each peer are evenly distributed over time.
Given a limit \(L =\SI{5}{\request\per100\second}\), crawling a botnet at \(F =\SI{20}{\request\per100\second}\) requires \(C =\left\lceil\frac{\SI{20}{\request\per100\second}}{\SI{5}{\request\per100\second}}\right\rceil=4\) crawlers.
Those crawlers must be scheduled \(O =\frac{\SI{1}{\request}}{\SI{20}{\request\per100\second}}=\SI{5}{\second}\) apart at a frequency of \(L\) for an even request distribution.
As can be seen in~\autoref{fig:crawler_timeline}, each crawler \(C_0\) to \(C_3\) performs only \SI{5}{\request\per 100\second} while overall achieving \(\SI{20}{\request\per100\second}\).
Vice versa given an amount of crawlers \(C\) and a request limit \(L\), the effective frequency \(F\) can be maximized to \(F = C \times L\) without hitting the limit \(L\) and being blocked.
Using the example from above with \(L =\SI{5}{\request\per100\second}\) but now only two crawlers \(C =2\), it is still possible to achieve an effective frequency of \(F =2\times\SI{5}{\request\per100\second}=\SI{10}{\request\per100\second}\) and \(O =\frac{\SI{1}{\request}}{\SI{10}{\request\per100\second}}=\SI{10}{s}\):
% \caption{Timeline of crawler events as seen from a peer}\label{fig:crawler_timeline}
\end{figure}
%}}} fig:crawler_timeline
While the effective frequency of the whole system is halved compared to~\autoref{fig:crawler_timeline}, it is still possible to double the frequency over the limit.
Building a complete graph \(G_C = K_{\abs{C}}\) between the crawlers by making them return the other crawlers on peer list requests would still produce a disconnected component and while being bigger and maybe not as obvious at first glance, it is still easily detectable since there is no path from \(G_C\) back to the main network (see~\autoref{fig:sensorbuster2} and~\autoref{fig:metrics_table}).
With \(v \in V\), \(\text{succ}(v)\) being the set of successors of \(v\) and \(\text{pred}(v)\) being the set of predecessors of \(v\), PageRank is recursively defined as~\cite{bib:page_pagerank_1998}:
For the first iteration, the PageRank of all nodes is set to the same initial value. 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.
For simplicity---and since it is not required to model human behaviour for automated crawling and ranking---a dampingFactor of \(1.0\) will be used, which simplifies the formula to
\todo{big graphs, how many Kn to get significant?}
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.\todo{for bigger (generated) graphs?}
In our experiments on a snapshot of the Sality~\cite{bib:falliere_sality_2011} botnet exported from \ac{bms} over the span of \daterange{2021-04-21}{2021-04-28}\todo{export timespan}, even 1 iteration were enough to get distinct enough values to detect sensors and crawlers.
\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}
\end{figure}
The distribution graphs in \autoref{fig:dist_sr_25}, \autoref{fig:dist_sr_50} and \autoref{fig:dist_sr_75} show that the initial rank has no effect on the distribution, only on the actual numeric rank values.
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.
On average, peers in the analyzed dataset have \num{223} successors over the whole week.
Since crawlers never respond to neighbourhood list requests, they will always be detectable by the described approach but sensors might benefit from the following technique.
By responding to neighbourhood list requests with plausible data, one can move make those metrics less suspicious, because it produces valid outgoing edges from the sensors.
The hard part is deciding which peers can be returned without actually supporting the network.
The following candidates to place into the NL will be investigated:
\begin{itemize}
\item Return the other known sensors, effectively building an complete graph \(K_{\abs{C}}\) containing all sensors
\item Detect churned peers from \ac{as} with dynamic IP allocation
\item Detect peers behind carrier-grade \ac{nat} that rotate IP addresses very often and pick random IP addresses from the IP range
\end{itemize}
Knowledge of only \num{90} peers leaving due to IP rotation would be enough to make a crawler look average in Sality.
This number will differ between different botnets, depending on implementation details and size of the network.
Adding edges from the known crawler to \num{90} random peers to simulate the described strategy gives the following rankings:\todo{table, distribution with random edges}
%{{{ other sensors
\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.
Also this does not help against the \ac{wcc} metric since this would create a bigger but still disconnected component.
Detecting if a peer just left the system, in combination with knowledge about \acp{as}, peers that just left and came from an \ac{as} with dynamic IP allocation (\eg{} many consumer broadband providers in the US and Europe), can be placed into the crawler's neighbourhood list.\todo{what is an AS}
If the timing of the churn event correlates with IP rotation in the \ac{as}, it can be assumed, that the peer left due to being assigned a new IP address---not due to connectivity issues or going offline---and will not return using the same IP address.
These peers, when placed in the neighbourhood list of the crawlers, will introduce paths back into the main network and defeat the \ac{wcc} metric.
It also helps with the PageRank and SensorRank metrics since the crawlers start to look like regular peers without actually supporting the network by relaying messages or propagating active peers.
Crawlers in \ac{bms} report to the backend using \acp{grpc}\footnote{\url{https://www.grpc.io}}.
Both crawlers and the backend \ac{grpc} server are implemented using the Go\footnote{\url{https://go.dev/}} programming language, so to make use of existing know-how and to allow others to use the implementation in the future, the coordinator backend and crawler abstraction were also implemented in Go.
\Ac{bms} already has an existing abstraction for crawlers.
This implementation is highly optimized but also tightly coupled and grown over time.
The abstraction became leaky and extending it proved to be complicated.
A new crawler abstraction was created with testability, extensibility and most features of the existing implementation in mind, which can be ported back to be used by the existing crawlers.
This is used to implement the bootstrapping mechanism of the old crawler, where once, when the crawler is started, the list of bootstrap nodes is loaded from a textfile.
The \mintinline{go}{PeerTask} instances returned by \mintinline{go}{FindPeer} contain the IP address and port of the peer, if the crawler should start or stop the operation, when to start and stop crawling and in which interval the peer should be crawled.
For each task, a \mintinline{go}{CrawlPeer} and \mintinline{go}{PingPeer} worker is started or stopped as specified in the received \mintinline{go}{PeerTask}.
These tasks use the \mintinline{go}{ReportPeer} interface to report any new peer that is found.
Current report possibilities are \mintinline{go}{LoggingReport} to simply log new peers to get feedback from the crawler at runtime, and \mintinline{go}{BMSReport} which reports back to \ac{bms}.
\mintinline{go}{BatchedReport} delegates a \mintinline{go}{ReportPeer} instance and batch newly found peers up to a specified batch size and only then flush and actually report.
\mintinline{go}{AutoCommitReport} will automatically flush a delegated \mintinline{go}{ReportPeer} instance after a fixed amount of time and is used in combination with \mintinline{go}{BatchedReport} to ensure the batches are written regularly, even if the batch limit is not reached yet.
\mintinline{go}{CombinedReport} works analogous to \mintinline{go}{CombinedFinder} and combines many \mintinline{go}{ReportPeer} instances into one.
\mintinline{go}{PingPeer} and \mintinline{go}{CrawlPeer} use the implementation of the botnet \mintinline{go}{Protocol} to perform the actual crawling in predefined intervals, which can be overwritten on a per \mintinline{go}{PeerTask} basis.
The server-side part of the system consists of a \ac{grpc} server to handle the client requests, a scheduler to assign new peers, and a \mintinline{go}{Strategy} interface for modularity over how work is assigned to crawlers.
Collaborative monitoring of \ac{p2p} botnets allows circumventing some anti-monitoring efforts.
It also enables more effective monitoring systems for larger botnets, since each peer can be visited by only one crawler.
The current concept of independent crawlers in \ac{bms} can also use multiple workers but there is no way to ensure a peer is not watched by multiple crawlers thereby using unnecessary resources.
Following this work, it should be possible to rewrite the existing crawlers to use the new abstraction.
This might bring some performance issues to light which can be solved by investigating the optimizations from the old implementation and applying them to the new one.
Another way to expand on this work is automatically scaling the available crawlers up and down, depending on the botnet size and the number of concurrently online peers.
Doing so would allow a constant crawl interval for even highly volatile botnets.
