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Again, the technique was invalidated by a recent update in the reference client Footnote 2. All mentioned techniques target reachable nodes, and require the adversary to connect to the whole network and observe data propagation either in a passive way or by actively introducing marker messages to infer communication links.

In Section 6 , we will leverage these concepts to design our topology-monitoring solution. In this section, we give an overview of our protocol, explaining its operating principles, and motivating our design choices. The AToM protocol is run by a set of monitors that connect to all reachable nodes. The monitors continuously run a topology-inferring protocol to maintain an updated snapshot of the network. An example of this scenario is depicted in Fig. Our scenario: a monitor node M connects to all reachable nodes N , excluding unreachable ones U.

We assume monitors know all public nodes. To that purpose, monitors can use a crawler or external services like Bitnodes [ 10 ]. We limit the scope of the AToM protocol to the reachable part of the network. There are different reasons behind this choice. As mentioned in Section 2 , reachable nodes constitute the backbone of the network, since they maintain the vast majority of connections. On the contrary, unreachable nodes are only marginally involved in data propagation [ 18 ].

This arguably makes the reachable part the most important to protect and optimize. From a security perspective, it is also safer to limit the public topology to reachable nodes, as this does not affect their protection from known attacks, as discussed in Section 4. Instead, unreachable nodes might be more exposed to certain attacks if the adversary had access to this information. Finally, there are several practical advantages in monitoring reachable nodes only.

First of all, as discussed in 2 , the reachable portion of the network is relatively small, and its nodes show more stability in terms of number and connections [ 10 , 50 ].

This eases the monitoring task, which can better adapt to changes in the network. Another advantage is the fact that is possible to guarantee the almost completeness of the snapshot computed by monitors. In fact, it is virtually impossible to know and connect to all unreachable nodes, due to the inability to reach them from the Internet. Because of this, malicious users could also fake the existence of unreachable nodes, without the monitors being able to verify them.

Instead, reachable nodes can be verified by simply opening a connection towards them and ensuring they run the Bitcoin protocol Footnote 3. Note that, despite being out of scope, unreachable nodes could also make use of the AToM protocol by querying monitors a snapshot of the topology.

Specifically, each unreachable node can compute its relative snapshot by adding its own connections to the queried snapshot. In fact, as these nodes only maintain outbound peers, all their connections are towards reachable nodes.

This extended snapshot could be used, for example, to autonomously decide which peers to connect to. Given the above discussion, the restriction of AToM to reachable nodes should be considered a feature of the protocol, rather than a limitation.

In the rest of the paper, when talking about nodes, we will always refer to reachable ones, unless specified.

Similar to other topology-inferring techniques described in Section 5 , our solution has monitors connect to all nodes and leverage marker messages to verify links. To minimize the overhead, monitors verify outbound connections only.

While this might seem counter-intuitive, it is easy to see that this allows covering the whole reachable network. In fact, all connections in the network are outbound, relatively to the node that initiated them, while inbound connections are just their symmetric view. This approach also allows us to implicitly exclude unreachable nodes from the protocol, since no outbound connection can be established towards them. To verify a link between two nodes, monitors have a marker message go through it, proving the two nodes are connected.

Adding an unpredictable random value to the marker, monitors ensure the only way a node can know it, is to have received it from the peer to which it was originally sent. For instance, if a monitor wants to verify a connection between nodes A and B , it sends to A a marker containing a random value r ; then, it probes B for such a value. If B replies with the correct value, the monitor considers the connection verified.

In fact, the only way for B to know r is to have received from A , which proves they are connected. Different from inferring techniques, which typically make use of side channels, we have nodes actively participate in the AToM protocol. This makes the result more reliable, but presents a downside: if nodes misbehave, because faulty or malicious, it is hard to prove or disprove a connection without making use of trusted solutions like certificates or trusted execution environments.

This means the set of valid monitors should be agreed on beforehand by the Bitcoin community. At a practical level, this can be obtained by leveraging already-existing semi-trusted entities of the Bitcoin network, such as the DNS servers used for node bootstrapping, or the list of peers hardcoded in the reference client [ 51 ]. Each monitor computes the network topology by executing, for each node, a Peers Verification PeeV protocol.

The PeeV protocol verifies the outbound connections of a node using a marker message. This message contains the following information: the monitor M that created the marker, the target node N whose connections are being verified, and a random value r.

The monitor ID allows nodes to recognize which monitor is running the protocol and to verify it is a valid actor. The target node ID and the random value are required to avoid malicious behaviors, as we will show later in this section. Specifically, the PeeV protocol for a target node N and a monitor M works this way:.

We call a single execution of this protocol a PeeV round. Red arrows show the route of the marker. By running a PeeV round for every node, a monitor obtains a snapshot of the full network topology. However, changes in the network can occur at any time, producing errors in the computed snapshot.

While the relative stability of the reachable network makes the number of mistakes in a single snapshot very limited, information on the connections among nodes should be updated over time in order to monitor the network.

An easy way for monitors to do so is to simply scan the network i. However, this approach cannot adapt well to all nodes. In fact, each node can experience changes at a different rate, making it hard to decide an appropriate scan frequency for the whole network.

In particular, if the frequency is too high, it might affect efficiency and increase network traffic. On the other hand, setting this value too low would affect the accuracy of the snapshot.

For instance, short-lived connections established within two consecutive scans would remain undetected. Therefore, we adopt a continuous-monitoring approach, scanning each node with the PeeV protocol individually, with an independent frequency value. In turn, this value is dynamically adjusted for each node according to the stability of its connections i. This way, nodes experiencing changes at a higher rate will be scanned more frequently than stable ones.

Such an approach allows us to improve accuracy, as the scanning process adapts to the variability of single nodes, and reduce the impact on the network, as message exchanges are spread over time while performing a one-shot snapshot requires exchanging all messages at once.

A potential issue for the AToM protocol is the presence of misbehaving nodes. These nodes can deviate from protocol, either accidentally if faulty or intentionally if malicious , producing errors in the snapshot. In particular, while faulty nodes do not behave consistently with each other making it easier to spot the error , malicious ones, when controlled by a single actor, can cooperate to deceive monitors.

Therefore, in this section, we specifically address malicious behaviors, with the goal of preventing them from affecting the accuracy of the protocol. While our countermeasures are designed to protect from malicious nodes, it should be clear that they are effective against faulty nodes as well.

We consider an adversary that controls an arbitrary number of colluding nodes and aims at affecting the accuracy of the snapshot computed by the monitors. To that purpose the adversary can try to hide or fake nodes, and to hide or fake connections. To hide a node, the adversary needs to avoid all connections from the monitors whose ID is known.

This can be done, for instance, by blacklisting monitor addresses. However, monitors can easily bypass this restriction by connecting from different unknown addresses.

Thus, the only way a node can hide completely is by rejecting all inbound connections. Nevertheless, this would effectively make the node unreachable, thus falling out of the AToM scope. To fake a node, the adversary can announce a fake address, pretending the existence of a running node.

However, as previously mentioned, monitors verify the existence of a node by connecting to them and running the Bitcoin protocol. Therefore, the only way to fake a node is to have a single instance of a client accepting connections through multiple addresses this can be done by using different ports or different IPs redirected to the running node.

This case is analogous to controlling multiple colluding nodes communicating with each other , which is part of our adversary model. Colluding nodes can be used by the adversary for hiding or faking connections. In particular, two such nodes can easily hide a connection among each other, or fake one by using external channels or a third colluding node.

There are virtually no means to detect or avoid this kind of behavior, as it is not possible to prevent malicious nodes from cooperating. However, there is no clear. Instead, it is realistic to assume the adversary would try to hide or fake connections with honest nodes, as this might be used for other attacks e. Therefore, when considering a pair of nodes, we will only focus on the case where at least one node is honest. It is easy to see that each of the above-listed behaviors potentially produce errors false positives and false negatives in the snapshot computed by a monitor.

In particular, cases 1 and 2 might induce the monitor to infer a non-existing outbound connection false positive. Similarly, case 3 might make the monitor keep in the topology snapshot a connection that does not exist anymore false positive. The effect of case 4 depends on which field the adversary modifies. On the one hand, changing the monitor identity or the random value would make the marker being dropped or rejected false negative ; as such, this analogous to dropping the message cases 5 and 6 , although it generates more traffic.

On the other hand, changing the target field could be used in combination with case 2 to produce a fake connection false positive. However, since the random value is bound to the target node, this modification would result in the marker being considered invalid.

Cases 5 and 6 effectively hide one or more connections from the monitor false negative. Given the importance of accuracy in our topology-computation protocol, it is necessary to introduce countermeasures to avoid the effects of the above-listed behaviors.

To ensure the accuracy of our protocol in the presence of malicious nodes, we address each of the behaviors listed above. To avoid case 1 , we make honest nodes accept markers only from inbound peers; markers received from outbound peers are simply dropped.

Similarly, to handle case 2 , we make nodes accept markers only when received from the specified target. To avoid replay attacks case 3 , we make monitors accept only markers of the current PeeV round for a specific target.

In this respect, the random value r acts as an identifier of the round. Case 4 , 5 and 6 are hard to avoid, since we cannot prevent a malicious node from dropping or modifying a message Footnote 4. Therefore, we mitigate these cases by leveraging information from the monitors.

In particular, we make each node maintain a peer if confirmed by the majority of monitors. At the end of a PeeV round, monitors send to the target node the list of its currently verified peers both inbound and outbound. Whenever a peer is not confirmed by the majority of monitors, its connection is closed, and the monitor is blacklisted typically, in Bitcoin, nodes are banned for 24 hours before being readmitted as peers.

This mechanism aims at discouraging malicious nodes from misbehaving, as it might make them lose connections. Additionally, it allows monitors to restore the correctness of the topology snapshot. In fact, while the snapshot is initially incorrect for not including an existing connection false negative , it becomes correct as soon as such connection is closed true negative.

We refer to this feature as enforced consistency. Note that such a behavior only applies to connections where the malicious node is connected to an honest peer, since, as already discussed, it is not possible to prevent two malicious nodes from faking or hiding a connection. As connections in Bitcoin are not encrypted, a MitM adversary is able to drop, modify, and forge messages.

As we saw, the use of random values in markers prevents the adversary from forging or modifying valid markers. In fact, these attacks are akin to the cases described above, and ultimately result in a connection not being verified. However, differently from previous cases, this attack might aim at hiding a connection between honest nodes, eventually leading to the connection to be lost.

In other words, this is can be seen as a DoS attack. Similarly to the drop case, there is no possible countermeasure to avoid the attack. Nonetheless, it is worth noting that a MitM attacker is always able to drop the connection it controls, and, in the case of the Bitcoin protocol, other dangerous attacks are possible, such as deanonymization and double-spending. Therefore, in that respect, our protocol does not create any additional attack surface.

In this section, we formally define the AToM protocol by means of the procedures executed by monitors and nodes. For the sake of simplicity we refer to a single monitor when describing the procedures, unless where differently specified. To run the PeeV protocol for a target node N , monitors execute the procedure shown in Algorithm 2.

This procedure generates a random number r , and sends to N a marker message with the following payload:. To avoid indefinite waits, the monitor sets a timeout t for receiving markers back from other nodes.

When a marker message is received from a node P , it is checked against the one sent to N. This procedure acts depending on the source of the message pfrom : if pfrom is a legit monitor M , the marker is forwarded to all outbound peers; if pfrom is an inbound peer and corresponds to the target N , the marker is forwarded to the monitor M.

To build and maintain an up-to-date snapshot of the topology G M , monitors run the A T o M procedure, shown in Algorithm 4. Note that peer lists do not change between PeeV rounds, which means that a connection stays in G M until a PeeV execution fails to verify it.

Before starting the following PeeV round for N , a time f N is waited w a i t N e x t , which depends on the corresponding scan frequency. When only 1 change is detected, the frequency is kept unchanged.

This mechanism allows AToM to better adapt to sudden and isolated pikes in the variability of peer connections. Again, we assume new nodes are automatically discovered by the monitor, and added to V M. When this occurs, the corresponding PeeV loop is started. Upon receiving a verified message from a monitor M , nodes execute the H a n d l e V e r i f i e d procedure shown in Algorithm 5.

This value is then updated every time a verified message is received. Therefore, a connection is maintained as long as it is confirmed by the majority of monitors. This reputation system is designed to prevent the attacker from keeping a connection hidden and alive at the same time, effectively making the attack inconvenient.

We consider as a full trusted snapshot of the topology the union of nodes and connections confirmed by the majority of the monitors. Then, we define the global snapshot G A T o M as the union of the connections that are verified by the majority of monitors.

We assume monitors synchronize among each other to compute G A T o M and always use this snapshot as the trusted one. In particular, whenever some party requests the current state of the topology to a monitor, the global snapshot G A T o M is returned. In this section, we study the correctness and accuracy of our protocol, both in an honest setting and in the presence of misbehaving nodes.

Finally, we analyze the overhead for participating nodes in terms of the number of extra messages exchanged. As M is not included in the snapshot G M , this does not affect our analysis. For the sake of simplicity, we show the correctness of AToM in a trusted setting i. In Section 8. Additionally, we assume a connection is never dropped during the execution of the protocol we will relax this assumption in the accuracy analysis.

Showing the correctness of our protocol in a trusted environment is relatively straightforward. We prove the two sides of the implication separately. Similar to the previous case, we assume, for the sake of simplicity, that connections are not dropped. In this section, we analyze the correctness of AToM in the presence of misbehaving nodes. In particular, we study how malicious nodes can affect the global snapshot G A T o M.

We refer to the possible misbehaviors for a malicious node, listed in Section 6. Given the fact that two colluding nodes cannot be prevented from faking or hiding connections, we only study the cases where at least one node is honest. In Section 9 , we experimentally evaluate how accuracy is affected by how multiple colluding nodes can affect the accuracy of the protocol. We consider a malicious node N and an honest peer P. We do so by proving that none of these behaviors make the PeeV protocol verify an incorrect peer.

Let M be a monitor, N be a malicious node, and P be an honest inbound peer of N. Let M be a monitor, N be a malicious node, C be a colluding node, and P be an honest outbound peer of C that is not connected to N. If M executes. Note that, as previously mentioned, the adversary could modify the marker message setting the colluding node C as the target.

In this case, P would consider the message valid and forward it to M. However, the value field would not match the target, making the monitor discard the marker. Let M be a monitor, N be a malicious node, and P be a previously-connected honest inbound peer of N. For cases 4 to 6 , we show that a mismatch between E and E A T o M can only occur during a limited time frame, thanks to the enforced consistency feature.

For the sake of simplicity, we only prove case 5 , as the other two cases are equivalent modified markers are dropped by the monitor and thus follow the same reasoning. For each monitor. As previously discussed, the accuracy of AToM depends on how the network topology changes over time. In this section, we study all the events that can produce a mismatch between the global snapshot G A T o M and the actual topology G.

In particular, we consider the following events:. Note that case 1 implies case 3 , since, by definition, a node with no connections is not part of the network.

Similarly, case 2 implies case 4 for all the connections held by the node leaving the network. Thus, without loss of generality, we focus on cases 3 and 4. Let us consider a monitor M and a node N. In both cases 3 and 4 , if the event occurs before executing P e e V N , no mismatch is produced in the local snapshot G M.

Relatively to the local snapshot G M , both mismatches will be fixed at the following PeeV round for N. Since the global snapshot is the majoritarian union of the local snapshots a connection is included only if confirmed by the majority of monitors , a mismatch in G A T o M can only last while half of the monitors contemporary have the error in their local snapshot. Let t 0 be the time in which a change occurs.

For the sake of simplicity, we assume the execution of P e e V N as an atomic event. Given the above, the accuracy of the global snapshot can be adjusted by setting a maximum frequency value for all monitors. In this section, we analyze the impact of AToM over network nodes, in terms of the number of extra messages introduced by our protocol and comparing it to the average number of messages currently exchanged by a node of the Bitcoin network.

We calculate the average number of messages exchanged by a node N in a complete PeeV round , which includes the execution of a PeeV round for N to verify its outbound peers as well as the PeeV rounds needed to confirm all inbound peers. During a complete round, nodes exchange the following messages:. As, on average, Bitcoin nodes experience around one change per hour in their outbound connections [ 50 ], monitors will need to run, for each node, approximately one PeeV round per hour.

Hence, each node will exchange, for each monitor, around M s g P e e V messages per hour. As mentioned in Section 2 , connections for a node are usually limited to 8 outbound and inbound. This value can actually be diverse in real life, with most nodes never reaching the limit [ 13 , 20 ], and few others exceeding this number [ 11 ]. If we run, for instance, 10 monitors, each node would exchange around extra messages per hour, which is less than 1 extra message per second. Following the same reasoning, we could run as many as 50 monitors with only 3 extra messages per second for each node.

Considering the average number of messages exchanged by a Bitcoin node is around 50 per second [ 52 ], we can say the overhead introduced by the AToM protocol is very low. Moreover, AToM can easily scale to larger networks. In fact, the cost of running the AToM protocol for a monitor increases linearly in the number of nodes, with an average of only 10 extra messages per new node 1 marker to the node, 8 markers from its outbound peers, and 1 verification message , in each PeeV round.

On the other hand, the impact on a single node does not depend on the size of the network, but only on the number of monitors and the number of peers which is limited by the Bitcoin client.

To evaluate our solution, we implemented a proof of concept PoC and performed experiments in a simulated environment. In this section, we describe our implementation and show the results of our experiments. We limited the compatibility of the protocol to one node per IP address i.

This limitation is due to the fact that inbound peers are assigned a random port, making it impossible to distinguish two different nodes connecting from the same IP. Considering that virtually all known public nodes run from a different IP, we consider this a minor issue. The scan frequency and timeout values have been chosen according to our local network delays.

Similarly, a d j u s t F r e q u e n c y works as described in Algorithm 4, using seconds as the time unit f N is increased by 1 second and decreased by c seconds. The actual PeeV round scheduling is randomized following a Poisson distribution over f N. This further spreads messages over time and makes it harder for an adversary to predict PeeV round timings.

For safety purposes, nodes enable the reputation system for a node only after 3 PeeV rounds have passed since the connection was established for each monitor. This was necessary to avoid disconnecting inbound peers that are not being scanned at the same rate as the node itself.

After this safe period, nodes are immediately disconnected when not verified by the majority of peers. Such peers are also banned, avoiding future connections to and from them. We implemented malicious nodes, which consistently deviate from the protocol to hide and fake connections.

These nodes are able to recognize each other and cooperate to deceive the monitors. In particular, malicious nodes conceal their connections by dropping all markers received from honest peers and fake connections by forwarding those received from a monitor to other malicious nodes. When a malicious node receives a marker from a colluding peer, it forwards it to the monitor, producing a false positive in the local snapshot. Note that such behavior represents the worst-case scenario for our protocol.

We performed three series of experiments with network variability values of 1, 5, and 10 seconds, which have been chosen to be equal to the lowest, initial, and highest scan frequency for each node f N. Each simulation lasted 10 minutes, with monitors probed every 30 seconds. These metrics are typically used to evaluate topology-inferring techniques, allowing a direct comparison with our protocol. However, it should be noted that such techniques are run from the adversarial perspective.

This means, on the one hand, that honest nodes do not participate to the protocol, and, on the other hand, that there are no malicious nodes trying to cheat. Therefore, false negatives and false positives are caused by other factors compared to our setting, giving precision and recall values a different meaning for topology-inferring techniques. This should be taken into account when comparing these techniques to our protocol. To perform the experiments, we implemented a private Bitcoin network i.

The network is composed of 50 nodes and 4 monitors the number of malicious nodes is calculated as the percentage of the total number of nodes. Outbound connections are opened from each monitor towards all nodes, as soon as they are created. In turn, each node connects to 3 outbound peers, chosen uniformly at random.

Between two nodes, only one connection can be established, meaning that there are no mutual connections, nor multiple inbound connections from the same node. To cope with the local scale of the simulation, we let changes in the network occur at a much faster rate than the real Bitcoin network. In particular, we have network events occur at a target average frequency, referred to as network variability and denoted by var in our results , whose value is set at the beginning of each experiment. At each iteration a node is either added or removed, maintaining an average of 50 nodes throughout the experiment.

The percentage of malicious nodes is also kept stable over time. When a node is removed, its inbound peers are connected to another node to always keep 3 outbound connections this emulates the behavior of nodes in the Bitcoin network. Despite the small scale, our framework is designed to behave as close as possible to the real Bitcoin network.

As such, although experiments are needed at a larger scale, we are confident that the results we obtained fairly represent the characteristics of our protocol. We show the AToM precision and recall obtained in our simulation Fig.

In particular, the different network variability did not seem to produce notable differences in the results. When malicious nodes are introduced, the number of false negatives and false positives starts to grow, lowering recall and precision, respectively.

This is due to the fact that colluding nodes, when connected, can only hide their own connection thus generating a false negative , since the enforced consistency property quickly removes hidden connections with honest nodes. On the other hand, as discussed in Section 6 , colluding nodes can exchange valid markers received from their inbound peers to make the monitor infer wrong connections.

This translates to as many false positives as their inbound peers. While controlling such a massive amount of nodes can be used to affect the accuracy of the global snapshot but not, for instance, to cause disconnections among nodes.

Given the fact that controlling many reachable nodes can only affect the accuracy a similar attack could only affect the precision of the global snapshot, without being able, for instance, to cause disconnections among nodes, it is unclear whether such a motivation would be worth the cost for the adversary.

In conclusion, our results prove that AToM adapts well to the variability of the network and has high resilience against malicious nodes. However, a massive number of colluding malicious nodes can affect its precision. This factor should be taken into account when leveraging AToM for security purposes.

In this paper, we studied the problem of topology obfuscation in the Bitcoin network. This need to randomly connect means that SPV nodes also are vulnerable to network partitioning attacks or Sybil attacks, where they are connected to fake nodes or fake networks and do not have access to honest nodes or the real bitcoin network.

For most practical purposes, well-connected SPV nodes are secure enough, striking the right balance between resource needs, practicality, and security. For infallible security, however, nothing beats running a full blockchain node. A full blockchain node verifies a transaction by checking the entire chain of thousands of blocks below it in order to guarantee that the UTXO is not spent, whereas an SPV node checks how deep the block is buried by a handful of blocks above it.

To get the block headers, SPV nodes use a getheaders message instead of getblocks. The responding peer will send up to 2, block headers using a single headers message. The process is otherwise the same as that used by a full node to retrieve full blocks. SPV nodes also set a filter on the connection to peers, to filter the stream of future blocks and transactions sent by the peers. Any transactions of interest are retrieved using a getdata request. The peer generates a tx message containing the transactions, in response.

Figure shows the synchronization of block headers. Because SPV nodes need to retrieve specific transactions in order to selectively verify them, they also create a privacy risk. Bloom filters allow SPV nodes to receive a subset of the transactions without revealing precisely which addresses they are interested in, through a filtering mechanism that uses probabilities rather than fixed patterns.

A bloom filter is a probabilistic search filter, a way to describe a desired pattern without specifying it exactly. Bloom filters offer an efficient way to express a search pattern while protecting privacy.

They are used by SPV nodes to ask their peers for transactions matching a specific pattern, without revealing exactly which addresses they are searching for. If she asks a less specific pattern, she gets a lot more possible addresses and better privacy, but many of the results are irrelevant. If she asks for a very specific pattern, she gets fewer results but loses privacy.

Bloom filters serve this function by allowing an SPV node to specify a search pattern for transactions that can be tuned toward precision or privacy. A less specific bloom filter will produce more data about more transactions, many irrelevant to the node, but will allow the node to maintain better privacy. The SPV node will then make a list of all the addresses in its wallet and create a search pattern matching the transaction output that corresponds to each address.

Usually, the search pattern is a pay-to-public-key-hash script that is the expected locking script that will be present in any transaction paying to the public-key-hash address. If the SPV node is tracking the balance of a P2SH address, the search pattern will be a pay-to-script-hash script, instead.

The SPV node then adds each of the search patterns to the bloom filter, so that the bloom filter can recognize the search pattern if it is present in a transaction. Finally, the bloom filter is sent to the peer and the peer uses it to match transactions for transmission to the SPV node. Bloom filters are implemented as a variable-size array of N binary digits a bit field and a variable number of M hash functions.

The hash functions are designed to always produce an output that is between 1 and N, corresponding to the array of binary digits. The hash functions are generated deterministically, so that any node implementing a bloom filter will always use the same hash functions and get the same results for a specific input.

By choosing different length N bloom filters and a different number M of hash functions, the bloom filter can be tuned, varying the level of accuracy and therefore privacy. In Figure , we use a very small array of 16 bits and a set of three hash functions to demonstrate how bloom filters work.

The bloom filter is initialized so that the array of bits is all zeros. To add a pattern to the bloom filter, the pattern is hashed by each hash function in turn. Applying the first hash function to the input results in a number between 1 and N. The corresponding bit in the array indexed from 1 to N is found and set to 1 , thereby recording the output of the hash function.

Then, the next hash function is used to set another bit and so on. Adding a second pattern is as simple as repeating this process. The pattern is hashed by each hash function in turn and the result is recorded by setting the bits to 1.

Note that as a bloom filter is filled with more patterns, a hash function result might coincide with a bit that is already set to 1 , in which case the bit is not changed. In essence, as more patterns record on overlapping bits, the bloom filter starts to become saturated with more bits set to 1 and the accuracy of the filter decreases.

This is why the filter is a probabilistic data structure�it gets less accurate as more patterns are added. The accuracy depends on the number of patterns added versus the size of the bit array N and number of hash functions M. A larger bit array and more hash functions can record more patterns with higher accuracy. A smaller bit array or fewer hash functions will record fewer patterns and produce less accuracy. To test if a pattern is part of a bloom filter, the pattern is hashed by each hash function and the resulting bit pattern is tested against the bit array.

If all the bits indexed by the hash functions are set to 1 , then the pattern is probably recorded in the bloom filter. Because the bits may be set because of overlap from multiple patterns, the answer is not certain, but is rather probabilistic. The corresponding bits are set to 1 , so the pattern is probably a match. On the contrary, if a pattern is tested against the bloom filter and any one of the bits is set to 0 , this proves that the pattern was not recorded in the bloom filter.

A negative result is not a probability, it is a certainty. One of the corresponding bits is set to 0 , so the pattern is definitely not a match. See Appendix B or visit GitHub. Bloom filters are used to filter the transactions and blocks containing them that an SPV node receives from its peers. The SPV node will then send a filterload message to the peer, containing the bloom filter to use on the connection. Only transactions that match the filter are sent to the node. In response to a getdata message from the node, peers will send a merkleblock message that contains only block headers for blocks matching the filter and a merkle path see Merkle Trees for each matching transaction.

The peer will then also send tx messages containing the transactions matched by the filter. The node setting the bloom filter can interactively add patterns to the filter by sending a filteradd message. To clear the bloom filter, the node can send a filterclear message. Because it is not possible to remove a pattern from a bloom filter, a node has to clear and resend a new bloom filter if a pattern is no longer desired.

Almost every node on the bitcoin network maintains a temporary list of unconfirmed transactions called the memory pool , mempool , or transaction pool. Nodes use this pool to keep track of transactions that are known to the network but are not yet included in the blockchain. As transactions are received and verified, they are added to the transaction pool and relayed to the neighboring nodes to propagate on the network.

Some node implementations also maintain a separate pool of orphaned transactions. Any matching orphans are then validated. If valid, they are removed from the orphan pool and added to the transaction pool, completing the chain that started with the parent transaction. In light of the newly added transaction, which is no longer an orphan, the process is repeated recursively looking for any further descendants, until no more descendants are found.

Through this process, the arrival of a parent transaction triggers a cascade reconstruction of an entire chain of interdependent transactions by re-uniting the orphans with their parents all the way down the chain. Both the transaction pool and orphan pool where implemented are stored in local memory and are not saved on persistent storage; rather, they are dynamically populated from incoming network messages.

When a node starts, both pools are empty and are gradually populated with new transactions received on the network. Unlike the transaction and orphan pools, the UTXO pool is not initialized empty but instead contains millions of entries of unspent transaction outputs, including some dating back to The UTXO pool may be housed in local memory or as an indexed database table on persistent storage. Furthermore, the transaction and orphan pools only contain unconfirmed transactions, while the UTXO pool only contains confirmed outputs.

Alert messages are a seldom used function, but are nevertheless implemented in most nodes. This feature is implemented to allow the core developer team to notify all bitcoin users of a serious problem in the bitcoin network, such as a critical bug that requires user action.

The alert system has only been used a handful of times, most notably in early when a critical database bug caused a multiblock fork to occur in the bitcoin blockchain. Alert messages are propagated by the alert message. The alert message contains several fields, including:. Alerts are cryptographically signed by a public key. The corresponding private key is held by a few select members of the core development team. The digital signature ensures that fake alerts will not be propagated on the network.

Each node receiving this alert message will verify it, check for expiration, and propagate it to all its peers, thus ensuring rapid propagation across the entire network. In addition to propagating the alert, the nodes might implement a user interface function to present the alert to the user. In the Bitcoin Core client, the alert is configured with the command-line option -alertnotify , which specifies a command to run when an alert is received.

The alert message is passed as a parameter to the alertnotify command. Most commonly, the alertnotify command is set to generate an email message to the administrator of the node, containing the alert message.

The alert is also displayed as a pop-up dialog in the graphical user interface bitcoin-Qt if it is running. Other implementations of the bitcoin protocol might handle the alert in different ways. Many hardware-embedded bitcoin mining systems do not implement the alert message function because they have no user interface. It is strongly recommended that miners running such mining systems subscribe to alerts via a mining pool operator or by running a lightweight node just for alert purposes.

Skip to main content. Start your free trial. Chapter 6. The Bitcoin Network. Peer-to-Peer Network Architecture. Nodes Types and Roles.

Figure A bitcoin network node with all four functions: wallet, miner, full blockchain database, and network routing. The Extended Bitcoin Network. The extended bitcoin network showing various node types, gateways, and protocols. Network Discovery. Full Nodes. Node synchronizing the blockchain by retrieving blocks from a peer.

Tip A full blockchain node verifies a transaction by checking the entire chain of thousands of blocks below it in order to guarantee that the UTXO is not spent, whereas an SPV node checks how deep the block is buried by a handful of blocks above it.

Bloom Filters. An example of a simplistic bloom filter, with a bit field and three hash functions. Bloom Filters and Inventory Updates. Transaction Pools. Alert Messages. ID An alert identified so that duplicate alerts can be detected Expiration A time after which the alert expires RelayUntil A time after which the alert should not be relayed MinVer, MaxVer The range of bitcoin protocol versions that this alert applies to subVer The client software version that this alert applies to Priority An alert priority level, currently unused.

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