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Monero cryptocurrency mining vulnerability fortiguard
The malware can download it as an executable compiled file with PyInstaller, thus, there is no need to install Python in the machine where PyRoMine will be run. It is possible that this is utilized for reinfection and other attacks, according to Manuel.
Other investigators have discovered more malware pieces which utilize EternalBlue for cryptocurrency mining with great success, such as Adylkuzz, Smominru and WannaMine. More information available at Fortinet Highlighted News The government of the United States and United Kingdom allege that Russia is behind the increase in attacks to their network infrastructure.
In the first statement connected to this, the United States cyber-security authorities have issued a technical alert in order to warn users of a campaign being carried out by the Russian attackers who attack the network infrastructure. The targets are devices at all levels, including routers, switches, firewalls, network intrusion detection systems and other devices that support network operations. With the access which they have obtained, they are capable of masking themselves as privileged users, which permits them to modify the devices operations so that they can copy or redirect the traffic towards their infrastructure.
This access also could allow them to hijack devices for other purposes or to shut down network communications completely. In this way, everyone, regardless of where they live, will be asked to review important information about how Facebook uses data and about their privacy.
This involves finding a nonce such that the value generated by hashing the new blob, using the PoW hash function, is lower than the target. The information included in the New Job message is sufficient to define the problem. Once the miner finds a solution, it submits its result to the pool in the form of a Solution Submission message.
An example for this type of message can be seen in Listing 2. It contains several fields, but 3 are relevant for our analysis. The nonce 8 hexadecimal characters denoting a 4-byte value has been found by the miner and used to produce a suitable hash. The hash value of the block header using the found nonce is returned as result.
The mining pool server receives the Solution Submission message. After verifying the correctness of the embedded solution, it sends back a Submission Result message. An example is shown in Listing 3. This pattern of communication between the pool client and server, denoted in Fig. The plot is based on full-packet capture DPI for demonstration purposes, but our algorithm works on NetFlow data.
As an example, NetFlow records corresponding to the window of network traffic between the dashed vertical lines are presented in Table 1. This happens for two reasons: either a new block has been mined or new transactions appeared for the current block. Therefore, the ratio between the number of jobs and number of the submitted solutions in a single NetFlow record pair is not one-to-one. This makes reconstructing Stratum semantics from NetFlow more difficult. By analyzing several mining traces using visualization tools, e.
New Job and Solution Submission are the largest messages sent by the pool and the miner, respectively. These properties enable us to engineer Stratum-specific features for accurate detection of mining traffic. Design tradeoffs Several features of the current cryptomining ecosystem are crucial for the design of our detector. First, the Stratum protocol is de facto standard.
Furthermore, not only the protocol but also the few client implementations are largely shared among legitimate and malicious users. Finally, malicious mining operations are often carried out via legitimate public mining pools [ 20 ]; thus, the malicious actors are forced to use exactly the same client and protocol implementations as the said public pools.
It is based on network traffic inspection, not endpoint monitoring, 2. It uses aggregate information, i. It employs one-class, not binary classification. Its features are mining-specific. Tradeoffs of these decisions are summarized in Table 2 , empty cells denote cases with no discernible effects.
Network-based detection, in contrast, can be centralized. However, existing simple network-based approaches, e. Consequently, XMR-RAY inspects NetFlow instead of packets, achieving several advantages while potentially reducing accuracy due to the information loss. The choice of machine learning approach is based on the premise that pool mining traffic can be discriminated from all other network communications. Therefore, we employ a one-class classifier OCC trained solely on mining traffic generated by generic legitimate mining pool clients.
This is a clear competitive advantage over related work which employs binary classifiers, as they additionally require full network traffic for training. In essence, binary classifiers decide between two classes, while OCCs decide between the target class and everything else, i. If the target class is well characterized by representative training data and characteristic features, and the universe is very diverse and constantly changing—an assumption which is true in our case—then OCC is clearly a superior choice.
The benefits are very significant, the only drawback is the information loss which may lead to lower accuracy. To alleviate information loss, the main innovation effort was invested in reconstructing as much information as possible from NetFlow records by designing Stratum-specific features. Section 6 demonstrates that this effort was very successful and that another crucial benefit was achieved this way: robustness against encryption, proxying, and tunneling.
Clearly, the benefits are numerous and very significant, especially for the last 2 design choices, which also represent competitive advantages compared to related work. However, the main strength is also the main weakness: by being Stratum-specific, the approach is not applicable to completely novel protocols.
This is discussed from the perspective of adversarial machine learning in Section 6. Deployment scenario A typical deployment scenario for our detector is shown in Fig. A network flow is a communication session between two applications described by the tuple As,ps,Ad,pd,P , where As,Ad are source and destination IP addresses, ps,pd are the corresponding ports, and P is the IP protocol. Each direction in the communication is considered a separate, unidirectional flow.
Inactive timeout occurs if no packet is observed in a flow within a specified time interval. After NetFlow collection is enabled on a device, flow statistics are stored and updated in a cache. When a flow times out, its statistics are exported in form of a NetFlow record. Data collection The starting point of our mining investigation is a representative corpus of legitimate mining traffic collected using a mining server optimized for CPU and GPU mining.
It runs Ubuntu In our implementation, metadata is encoded using NetFlow v5, the most common and lightweight version, but alternative formats like IPFIX are also suitable. This procedure is used for training and evaluation in our experiments.
To collect a comprehensive corpus of cleartext mining traffic, we mined in 25 well-known Monero public pools [ 97 ] for about 6 months, gathering around h of mining traffic. For the experimental evaluation, we also collected a corpus of NetFlow data test dataset from a large enterprise network about 10k hosts. Like for mining traffic, export timeouts were set to s. It comprises around million flows, of which there are 16, TCP conversations , flows longer than 30 min and was collected for 1 month.
This heterogeneous network environment is representative for large enterprises. This is standard practice among enterprises that for security purposes deploy dedicated NetFlow exporters. Specifically, we insert NetFlow records belonging to mining TCP conversations among the NetFlow records from the benign enterprise traffic.
We carefully avoid using the injected mining traffic for training. It takes as input NetFlow records and operates in 2 modes: training and deployment. In training mode, NetFlow records from our database of collected mining traffic are used to train a machine learning model. In deployment mode, the trained model is used to classify NetFlow records resulting from TCP conversations of new, live network traffic as either mining or other traffic. In the following, we provide a detailed description of individual modules.
A single mining session therefore corresponds to many NetFlow records. The first step of our method is to reassemble unidirectional NetFlow records into bidirectional TCP conversations. Traffic windowing by time In the second step, the previously reassembled TCP conversations are partitioned by time into overlapping successive windows, each window having the same time length of lW seconds.
This is repeated every fW seconds. Keeping lW constant ensures that time-dependent features have a comparable value among all time windows. The sets of time-windowed TCP conversations produced in this step are the basic unit of processing for the machine learning model, i. Feature extraction In this step, the output of the previous module is prepared for processing by the machine learning algorithm. The feature extraction module computes features capable of capturing the intrinsic network behavior of Stratum communications.
The features are described in Section 5. The output are real-valued feature vectors that describe the salient properties of individual traffic windows. Feature extraction is the last module in the preprocessing subsystem. The preprocessing is performed in exactly the same way for both training and deployment. Training In the training mode, incoming feature vectors are used to train a one-class classification model.
In this mode, only mining traffic from the training dataset is used as input. The model is trained to identify whether a set of NetFlow records corresponding to a time window of lW seconds from a single TCP conversation contains mining traffic or not.
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