The Sandworm Team hacking group is part of Unit 74455 of the Russian Main Intelligence Directorate (GRU), the US Department of Justice (DoJ) claimed as it unsealed an indictment against six hackers and alleged members on Monday.
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Sandworm Team attacks
'These GRU hackers and their co-conspirators engaged in computer intrusions and attacks intended to support Russian government efforts to undermine, retaliate against, or otherwise destabilize: Ukraine; Georgia; elections in France; efforts to hold Russia accountable for its use of a weapons-grade nerve agent, Novichok, on foreign soil; and the 2018 PyeongChang Winter Olympic Games after Russian athletes were banned from participating under their nation's flag, as a consequence of Russian government-sponsored doping effort,' the DoJ alleges.
'Their computer attacks used some of the world's most destructive malware to date, including: KillDisk and Industroyer, which each caused blackouts in Ukraine; NotPetya, which caused nearly $1 billion in losses to the three victims identified in the indictment alone; and Olympic Destroyer, which disrupted thousands of computers used to support the 2018 PyeongChang Winter Olympics.'
At the same time, the UK National Cyber Security Centre says that they asses 'with high confidence' that the group has been actively targeting organizations involved in the 2020 Olympic and Paralympic Games before they were postponed.
'In the attacks on the 2018 Games, the GRU's cyber unit attempted to disguise itself as North Korean and Chinese hackers when it targeted the opening ceremony. It went on to target broadcasters, a ski resort, Olympic officials and sponsors of the games. The GRU deployed of hacking operations. Chairman of the State Parliament committee on international affairs Dmitry Novikov says this is part of 'information war against Russia'. https://t.co/ifSuCM23VN
Build it (itch) mac os. — Lukasz Olejnik (@lukOlejnik) October 20, 2020 Blue libra mac os.
It's unusual to see the US mount criminal charges against intelligence officers that were engaged in cyber-espionage operations outside the US, but the rationale here is that many of the attacks resulted in real-world consequences that were aimed at undermining the target countries' governments and destabilizing the countries themselves, and that they affected individuals, civilian critical infrastructure (including organizations in the US), and private sector companies.
'The crimes committed by Russian government officials were against real victims who suffered real harm. We have an obligation to hold accountable those who commit crimes – no matter where they reside and no matter for whom they work – in order to seek justice on behalf of these victims,' commented US Attorney Scott W. Brady for the Western District of Pennsylvania.
There are currently no laws and norms regulating cyber attacks and cyber espionage in peacetime, but earlier this year Russian Federation president Vladimir Putin called for an agreement between Russia and the US that would guarantee the two nations would not try to meddle with each other's elections and internal affairs via 'cyber' means.
This latest round of indictments by the US is unlikely to act as a deterrent but, as Dr. Panayotis Yannakogeorgos recently told Help Net Security, indictments and public attribution of attacks serve several other purposes.
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Another interesting result of this indictment may be felt by insurance companies and their customers that have suffered disruption due to cyber attacks mounted by nation-states. Some of their insurance policies may not cover cyber incidents that could be considered an 'act of war' (e.g., the NotPetya attacks).
Deep learning is a hot topic these days and it is greatly increased by the fact that AMD/nVidia video cards can be used for accelerating the training of very complex neural networks. In the meantime, some powerful frameworks have been developed by the community. One of the most known is Caffe which is specialized in image recognition, just one of the areas where I am more interested in.
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However, it is not an easy task to get a working Caffe environment for a standard user. Of course, there are plenty of online documentation and some very nice guides but I didn't find any for a Mac OS X 10.11.4 version working out of the box, so after struggling several hours trying to have everything ready, I decided to post here all the steps just in case it could help someone else.
In my case, I wanted to run the cool new automatic colorization methods using deep learning algorithms and the results are absolutely fantastic.
In addition, I also wanted to use CPU and GPU code paths in order to compare the processing speed, unfortunately, the video card I have on my workstation is an old AMD card and I need to have a nVidia one for using CUDA libraries so my only machine with that specs is a MacBook Pro with Mac OS X 10.11.4, 16GB RAM, Intel Core i7 4960HQ and a nVidia GeForce GT 750M (2GB).
Let's start with the optional but highly recommended prerequisites:
- Check your Mac OS X and update everything is pending, you also need to install Xcode, 7.3 is the latest one but not supported by CUDA yet so later on, I will show you a work around for this issue
- Install Homebrew package manager and if possible, start up with a clean /usr/local directory
- Install Anaconda Python 2.7 on /usr/local directory. Once it is done, add to your ~/.bash_profile file:
Now, we are installing the needed libraries:
- Install nVidia CUDA 7.5, in my case the exact version is 7.5.27. I also installed it on /usr/local/cuda. Once it is done, add to your ~/.bash_profile file:
- Install nVidia cuDNN, you have to register and get approved by nVidia. I got 7.5 v5 but unfortunately, I was not able to use it, apparently some kind of incompatibility with my CUDA version or the Xcode used. In any case, once you have the tgz file, just uncompress it (tar -xfvz) and copy the contents of the /lib directory in to /usr/local/cuda/lib and cudnn.h to /usr/local/cuda/include directory.
- Install Intel MKL libraries, which is a part of Intel Parallel Studio XE Composer, you can download a trial version or a student/academic research one if you fulfill the requirements. MKL libraries are installed on /opt/intel/mkl. Once it is done, add to your ~/.bash_profile file:
- Using Homebrew, we are installing some needed libraries. At this point, it is very important to have the right environment variables, if you followed the guide step-by-step, you have them on your ~/.bash_profile but if you were working on the same terminal session, they are not loaded so, you can close this terminal and open a new one or just execute this command:
Before starting to download, compile and install the pending libraries, execute this command:
and you should see something like 'Python 2.7.xx :: Anaconda 4.x.x (x86_64)', if that is the case, the go on, otherwise, revise your environment vars as something is not well configured.
At this point, we are using the python provided by Anaconda so let's execute these commands (note that in some cases, the GNU C/C++ 5.3.0 will be compiled and it takes a while, ~30min on my rig):
- Finally, we can download Caffe, I am donwloading it to /usr/local/caffe:
Next step is to configure it, open Makefile.config file with your preferred text editor (vim?) and let's configure the following vars:
Update 23/August/2016: Instead of providing a partial Makefile.config file, a full copy of this file is attached above.
Give it a try…:
If it compiles fine, you can skip this paragraph, you probably have Xcode 7.0.2 or a lower version which is OK…
but if all you get is this error:
'nvcc fatal : The version (‘70300') of the host compiler (‘Apple clang') is not supported'
it means you have the Xcode 7.3 and CUDA still does not support it.
Of course, it has a solution, to install a supported Xcode version and use it for compiling Caffe:
– go to https://developer.apple.com/downloads/ and log in
– download Command Line Tools OS X 10.11 Xcode 7.2 and install it
– now, switch to use this version:Remember that using:
will configure the default compiler, which, in my case is 7.3.
Let's try to compile it again:This time, it should be OK. Now, let's compile the tests:
Everything went fine? Perfect! At this point, you probably want to run the tests but… they will fail(!). This time the guilty one is Apple and the latest OS X 10.11 'El Capitan' version, apparently it is undocumented but spending some time ‘googling', I found that other application are failing too as it seems that Apple unset all DYLD_ prefix environment variables when running scripts. I only know one solution:
– to disable System Integrity Protection, which I totally do not recommend to do, or if you do that, revert to SIP on as soon as you check that all the tests were OK.
So, we can discard to run the tests and trust that everything is fine and continue with only a few more steps. - Compiling PyCaffe, which is the python interface to Caffe:
If everything went fine, you are almost there! Add this environment variable to your ~/.bash_profile:
And now the final test:
You should get the python prompt and no errors at all which means that your python interface to Caffe is fully working and ready for some cool applications:
Update: if you receive some errors regarding to locale, you just need to add these new environment vars to your famous ~/.bash_profile file:
You are now ready for playing with some examples:
As you can see, it is not an easy task to configure Caffe environment but finally I got it and can test the Automatic Colorization algorithms for automatically converting grey-pictures into fully colored ones, in a few days I will write down a small post with my result.
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Have fun with this great but a bit difficult to configure deep learning framework!!
