The regulatory environment for cryptocurrencies is largely still in its infancy, but many analysts say that rules will develop as the industry becomes more mainstream. The U. In the U. Last month, VanEck Associates Corp. With so many investors wanting ways to trade crypto and few financial products available, shares of crypto-mining companies are booming.
Bitcoin bulls argue that this rally will end differently from , when the digital coin tumbled, because the players then were mostly retail investors. Wall said plans by central banks to introduce their own versions of digital currencies will also probably benefit the sector rather than hinder it. They will be only authorized by the government whereas something like Bitcoin is by its very nature decentralized. Are you looking for a stock?
Traditionally, a hacker will attack your machine in order to breach your network and gain access to sensitive company data. The way it works is this. This, of course, is where your PC or other internet connected device enters the picture, because why stop at just the machines owned and controlled by the hackers themselves?
By last year, that number had risen to nearly two million, and so far in , the number is already at 1. Used with permission from Article Aggregator. Support Center. This is a very different kind of attack. What a cryptocurrency miner wants, though, is your processing power. In , there were less than half a million such attacks over the course of the entire year.
Our adversary model as depicted in Fig. As previously stated, we stress that our model takes into account a malicious device that might be controlled by either a dishonest employee or by a remote hacker who took over control of the device itself. There are several strategies to mine without the company consent, an activity that today is really difficult to detect and prevent. This malicious behaviour might be implemented by either a full node or a miner. Full node. It is a full-featured client of the cryptocurrency infrastructure.
It locally stores the whole blockchain and participates to the consensus algorithm, being able to validate all the transactions. The mining activity performed by a full node is called solo mining because the process is done independently from other nodes.
It is a lightweight software that implements a simple worker that receives jobs i. When the mining pool successfully mines a block, both the reward and the fees will be divided among all the participants, proportionally to the computational power offered. This software does not participate in the cryptocurrency protocol and does not require to store a blockchain to work. In our scenario, we assume the client being either a full node or a miner is already provided with the ledger, if needed, and does not require any warm-up operations.
Indeed, Crypto-Aegis does not resort to application-specific transients and it does not require to be deployed before the malicious device starts its illicit activity. The sponge-attack illicitly absorbs resources from the corporate infrastructure and makes money out of them in favor of the attacker. This definition is more general than the one of Cryptojacking, that only refers to mining activities.
The sponge-attack can be implemented by deploying either a Full Node or a Miner. The use of a mining pool software allows to carry out mining activities without installing the heavier full node software. An adversary can use this software to perform a faster and stealthier attack since the targeted device does not need to store the ledger, usually very large.
Furthermore, by joining a mining pool, profits are increased even if the available resources are limited. Deploying a full node into a network without the administrator consent has significant advantages for the adversary. In the following we refer to different actors and actions by using the fallowing terminology:.
Crypto-client : A software illicitly installed in a device belonging to the Corporate Network with the aim of performing the sponge-attack. Standard software : A software legitimately installed in a device of the Corporate Network. Reference device : A laptop used for running both the Standard software and the Crypto-clients used in this paper.
In this section we provide a description of our measurement setup and a preliminary statistical analysis of the collected traces. Measurement setup. Our measurement setup can be resumed by Fig. We consider two scenarios: Scenario 1 where a VPN tunnel adds an encryption layer to the communication, and Scenario 2 where the client is directly connected to the Internet.
For our measurements, we used two different well-known VPN brands, i. We arbitrarily set the VPN exit node to France for all our measurements. Conversely, in Scenario 2, the malicious device is directly connected to the Internet without resorting to any additional encryption layer.
The malicious device—acting as our reference device when not mining—is a Dell XPS15 laptop running Ubuntu We define ingoing flow all the network traffic from the Internet to our reference device. Moreover, we refer as outgoing flow the network traffic generated by the reference device and sent to the Internet.
We collected network traffic from three different cryptocurrencies Bitcoin, Bytecoin and Monero and three different applications Skype, YouTube, and standard office applications mixed together as it follows:. Office network traffic. We logged the network traffic generated by the reference device while using it for standard office tasks, e. The above applications have been selected as a reference excerpt of three traffic patterns coming from three different application scenarios that are audio calls, video streaming, and standard office network traffic.
Our goal is to discriminate the flows involving the Crypto-client from the other flows in the network. It is known that Machine Learning for network traffic classification is biased by several parameters, i. One major concern is related to the consistency of the extracted features given the limited trace length.
In particular, we paid particular attention to capture packets from clients at steady-state and after the initial sync period was accomplished. Indeed, Crypto-clients require a warm-up period to download the blockchain and validate it. This guarantees that our log excerpts represent a consistent snapshot of a steady-state client either syncing or mining for the blockchain network. In this section, we start the analysis of the collected network traffic by considering the two network flows: ingoing and outgoing, as explained in the previous section.
In order to guarantee a fair comparison between the various scenarios, we extracted the same number of consecutive samples for each network trace, i. For each scenario, we report the trace duration equivalent to the extracted samples, the quantile 0. Firstly, we observe how considering the same amount of samples involves very different collection time depending on the application scenario, i.
Interarrival times are significantly affected by the use of VPN, i. Packet sizes are affected as well, i. It is worth noting that the reduction of the interarrival time with the increasing of the packet sizes involves a reduction of the trace length to guarantee the delivery of the same amount of data. VPN tunnelling affects interarrival times of Monero depending on the flow. While ingoing flows experience a reduction of the interarrival time, outgoing flows slightly increase their values.
Packet size is affected by the same phenomena. Interarrival times are more homogeneous for Bitcoin. VPN tunnelling tends to squeeze the packets all together and to increase the packet size. Bitcoin is special: VPN tunnelling is affecting much less the original traffic pattern although there are some significant variations for the packet size.
It is worth noting the differences among the cryptocurrencies when the traffic is collected without VPN tunnelling. Given the above considerations, we consider more in-depth analysis of the flows in order to subsequently identify the features to be used for the Machine Learning process.
Outgoing flows present packet sizes very different from each other in the range between and bytes. Only few exceptions fall out of that range, while it is worth noting how quantile 0. Moreover we observe that, raw traffic from Bytecoin, Monero, and Bitcoin present almost the same values of quantile 0. Interestingly, such values get closer being characterized by less variations when their traffic is tunnelled through a VPN.
Ingoing flows behave differently from outgoing ones. Packet size spans between closer ranges, i. Median values are randomly distributed and we do not observe any significant pattern in the VPN tunneling of cryptocurrency clients. We performed the same analysis for the interarrival times obtained by differentiating the absolute arrival times logged by WireShark.
The ingoing flows Fig. Similar observations can be drawn by looking at the outgoing flows as depicted by Fig. Our Crypto-client detection algorithm involves the following steps:. Features extraction. Features identification and extraction are paramount activities to maximize the performance of the classifier.
In this work, we consider several features starting from the very standard ones, i. We also consider other derived features with the aim of validating how they affect the final classifier performance. Cross validation is a common practice to average the results of Machine Learning algorithms. It is usually performed by defining a random partition of k out of n observations. The partition divides the observations into k disjoint subsamples or folds , chosen randomly but with roughly equal size.
The default value of k is Random Forest RF. The TreeBagger combines the results of many decision trees, which reduces the effects of overfitting and improves generalization. TreeBagger grows the decision trees in the ensemble using bootstrap samples of the data. This task involves the generation of statistics from the classifier results.
In this section, we introduce a simplified version of our methodology considering a binary decision problem. Our goal is to analyze the traffic of a network to determine whether a malicious mining activity is happening. We recall that the traffic collected from the Bitcoin client is related only to the syncing process being a Full Node , while the one related to the mining process will be considered later on.
Moreover, we assume the hypothesis H 0 : the current network event has been generated by the Bitcoin client. We run the Fold cross validation algorithm using the RF algorithm with a default value of 20 trees and only two features: interarrival time and packet size. The other values refer to False Positive - FP, i.
FPR and TPR can be used to highlight the classifier performance at different threshold values when the system can accept different levels of false positive values. As for the ROC curve in Fig. We now add more features to the current scenario and we analyze the performance of the classifier. Let us define the already basic introduced features and the new ones, as follows:.
In order to evaluate the impact of the features on the classification algorithm we used the Mean Square Error MSE averaged over all the trees in the ensemble and divided by the standard deviation taken over the trees, for each feature. The larger this value, the more important the feature is in the classification process. Our RF classifier has been configured with 20 default trees, the 6 features already introduced in the previous section, and a moving window of 5 observations.
Moreover, we consider only Full Node clients; therefore, the observed network traffic will be related to syncing and consensus operations. Ingoing flows. Firstly, we observe how the overall results are quite satisfactory, i. Conversely, worst case performance are achieved for:. Our results prove that ingoing flows from the Internet to the device can be used to effectively identify malicious miners inside local networks.
Moreover, we observe that worst case performance are due to Crypto-clients misclassified for other Crypto-clients; indeed, there is only one exception: cases of YouTube Express classified as Monero Express. We consider the previous phenomenon as a by-product of the VPN tunnelling; indeed, it is reasonable to assume that the same VPN is slightly re-shaping different traffic patterns in the same way.
Outgoing flows. As for the ingoing flows, we observe how the overall results are, again, quite satisfactory, i. The above considerations prove that outgoing flows from the device to the Internet can be used to effectively identify malicious miners inside local networks. As for the ingoing flow analysis, VPN does not significantly increase the privacy of the device: TPR values fluctuate depending on the adopted VPN brand, but they still remain high—and even increase for some cases.
One more interesting aspect is that misclassifications happen among cryptocurrencies adopting the same VPN, i. For both the ingoing and outgoing flows, the worst performance are experienced by Bytecoin Express, Bitcoin Express, and Bytecoin. Of course, this does not imply that VPN tunnelling is protecting the privacy of the clients as previously discussed. Indeed, we do not observe any significant difference with or without the presence of a VPN tunnel: for all the three cryptocurrencies the AUC values in presence of a VPN tunnel are still very high, i.
Therefore, we consider bfgminer for both Bitcoin and XMrig for Bytecoin and Monero as illicitly installed clients in a device belonging to a Corporate Network. In practice, Miners collaborate in pools to lower the variance of their revenue by sharing rewards with a group of other Miners. We consider the same measurement setup of Fig. Firstly, we reduce all the traces to the same amount of packets to guarantee a fair classification process for the RF algorithm: we choose packets.
Moreover, it is worth noting the case of Monero: the interarrival times are decreasing from The best detection performance are achieved with Bytecoin, Monero, and Bitcoin without VPN tunneling as in the previous case Ingoing flow analysis. As in this case ingoing flows , VPN tunneling affects the privacy of the client decreasing the identification performance, and in the following, we comment on the worst cases:. We highlight how—for both the Outgoing and Ingoing cases— misclassification are only due to cryptocurrencies, i.
Finally, it is interesting to observe how VPNs are more effective to protect the privacy of the Miners respect to the Full Node scenario. The more time they spend mining, the more money they stand to make, and downtime must be avoided at all costs. To achieve maximum profitability, miners leave their rigs running all day and night, meaning round-the-clock access to reliable power sources and resilient, high-speed network connections are a must.
These requirements make cryptocurrency mining sound like a dream use case for colocation datacentres , but this is a realisation that some participants have been relatively slow to reach. While bitcoin , the most well-known and high-profile example of a cryptocurrency, has been around since , user interest in using colocation facilities for mining purposes — particularly UK-based ones — began picking up a year or so ago, operators claim.
Anecdotally, this interest is coming from hobbyist miners, looking to scale up and improve the resiliency of their operations, while — perhaps — looking to secure a secondary income, whereas others could be classified as investor-backed micro-businesses. One notable difference, though, is the relatively short length of leases the colocation providers are willing to offer miners, which is partly down to operator concerns about the volatility of the cryptocurrency market. So instead of signing them up to multi-year contracts, providers are opting for rolling, monthly renewals instead, it seems.
The performance of a mining rig is measured using the hash rate metric , which tracks the number of computations that take place per second to help participants contribute to the supply of new cryptocurrencies.
The higher the hash rate, the better. Therefore, performance improvements to the hardware used to mine cryptocurrencies are a big influence on how much money miners can make, but power costs are the biggest determinant of profitability. To remain competitive, efficient and — ultimately — profitable, there may come a point for miners where they need to decide if it is worth upgrading their hardware or exiting the market altogether. A lot of this comes down to how quickly miners believe they can recoup the cost of such a hardware investment.
Beijing-based bitcoin mining hardware manufacturer Bitmain is one to watch, in this regard. Not only does it make the kit powering many of the third-party mining operations that exist today, but the company also runs cryptocurrency server farms of its own. Governments across the world are still grappling with how to regulate cryptocurrencies, meaning countries where it is advantageous to operate today may become less accommodating hosts in the years to come.
Swedish colocation provider Hydro66 is an example of an overseas operator that is supplementing the revenue generated by its more traditional enterprise clients by throwing over some of the space in its renewably powered facility to cryptocurrency miners. While freedom of movement is a concept cryptocurrency miners have at their disposal to take advantage of, there are a number of reasons why some prefer to keep their rigs running closer to home in the UK, rather than ship their kit overseas where they could potentially make more profit.
This is a view Barker shares, before going on to share anecdotal tales of users who have paid to have their hardware shipped over to the continent, so they can take advantage of cheaper power prices, only to find it never arrives. There are a good number of reputable colocation providers dotted across Europe, with a track record in catering to the needs of cryptocurrency miners, but it does pays to be wary, adds Barker.
For this reason, Wallage predicts blockchain , rather than cryptocurrency mining, is where the long-term opportunity in all this lies for the datacentre community as a whole. So any situation or process which depends on middlemen can potentially be improved by implementing a blockchain system. IBM is one of a number of tech giants focused on building out its blockchain proposition at present, having brought to market its software-as-a-service SaaS IBM Blockchain Platform offering in August , which is geared towards making the technology accessible to a much wider range of industries.
Oracle also has a cloud-based blockchain offering that forms part of its wider platform-as-a-service PaaS portfolio, while Microsoft is courting developers of blockchain-based applications to run them on its Azure public cloud platform. Cloud giant Amazon Web Services AWS has also made an investment commitment to help members of its partner community create blockchain-based services for members of the healthcare, life sciences, supply chain management, security and compliance industries too.
As IT leaders prepare their organizations for the year ahead, we're offering insights into the technology trends we predict will The Open Group zoned in on digital transformation initiatives with its new architecture certification option and IT4IT's 3. Commercial IT products including hardware and software systems could soon fall under the purview of the Buy American Act if their Security researcher Alex Birsan breached several major tech companies, including Microsoft and Apple, through a novel technique Enterprises must shore up their ransomware prevention efforts by strengthening security awareness, adding email controls, and An influx of false positive security alerts can lead infosec pros to overlook real threats.
Learn how to avoid security alert Cisco forecasts revenue growth between 3. The global pandemic caused mayhem on network security environments.
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