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  • David Vassallo 1:20 pm on May 23, 2017 Permalink | Reply
    Tags: , Data Science, Ransomware,   

    Anomaly detection vs Ransomware 

    A big part of what we do at CyberSift is anomaly detection. The recent WannaCry attack highlighted the growing threat of ransomware in the security landscape. The WannaCry authors may have made amateur mistakes, and there may be more stealthy and profitable attacks than WannaCry, but the negative impact it has had on Windows users (as it turns out… especially Windows 7 users) is undeniable — even bringing UK’s NHS to a halt. Microsoft promptly issued a patch, and vendors started releasing signatures to detect WannaCry — mostly lists of file hashes or domains:

    Sample WannaCry filehashes
    Sample WannaCry domains

    It’s still a largely a game of cat and mouse. A simple update and the above lists become invalid. CyberSift already does a pretty good job of detecting ransomware activity via it’s DNS module (one notices the domains shown above look nothing like the usual english domains the majority of users visit — a dead giveaway for CyberSift)… but we wanted to take the concept of anomaly detection further and try help block ransomware as it happens — not after the fact.

    We don’t usually venture into the realm of endpoint protection, but in this particular case we did — and we’re releasing a Windows anti-ransomware tool called “RansomSift”. Ransomsift doesn’t contain any signatures — no file hashes or domains, it’s a pure anomaly-based system. In this two part blog post we’ll explore the methods we used.


    First — let’s see RansomSift in action against WannaCry on a Windows XP machine:


    Anomaly Indicators

    How do we achieve the above? Ransomsift uses two classes of anomaly detectors:

    1. File-based statistical indicators. In this blog post we’ll highlight what exactly we use to detect when files are being encrypted — and why it doesn’t always work
    2. OS based statistical indicators. In the next part of this two part series, we’ll explore which Operating System features RansomSift monitors for anomalies to further reduce false positives and block ransomware quicker.

    Note: for these tests all files were encrypted using AES 256 using the command below (this becomes important later on so keep this in mind…)

    openssl enc -aes-256-cbc -a -pass pass:word


    File-based statistical indicators

    A couple of academic research papers deal with detecting ransomware as it encrypts files [1][2]. The basic idea is that if one where to plot the histogram of data in a “normal” file and compare it to that of an encrypted file, there are differences that can be detected. Let’s have a quick rundown of the methods used:

    Shannon Entropy

    A simplistic explanation of entropy is “randomness” in a file. If we compare the probability distribution of an unencrypted file (MS WORD DOCX in this particular case) and an the same file encrypted we see the following:

    A Microsoft Word file (unencrypted vs encrypted)

    Clearly, the unencrypted file is a lot more “random” than the encrypted file. This feature holds true across multiple file types:

    Skewness

    Another statistical feature we can measure is “skewness”. The below diagram sums up the concept of skewness:

    source: https://www.kullabs.com/uploads/skewness1.jpg

    Since the histograms are different, the unencrypted and encrypted versions of a file have different skewness. Plotting this for different file types we get another marked difference (though it looks like HTML would give us some difficulty here):

    Kurtosis

    The last measure we looked at was “kurtosis”. Again, a simple diagram explains the concept succinctly:

    Source: https://stats.stackexchange.com/questions/84158/how-is-the-kurtosis-of-a-distribution-related-to-the-geometry-of-the-density-fun

    Again, plotting kurtosis for different file types we get quite a difference (though again had we to rely on this statistic only, we’d have problems with TXT, HTML and DOC) :


    Victories and Defeats

    The above results alone are quite convincing. By combining the weak models and having each of them “vote” if a file is encrypted or not, we end up with a strong model that can tell with a good deal of reliability if a file is encrypted or not. RansomSift leverages this concept by monitoring files that have been changed in the “My Documents” directory, and determines if the file has been encrypted or not.

    However, depending on this file-based statistical approach alone is not enough. During testing we ran into a couple of false positives (files being marked as encrypted when they are not) and false negatives (files being marked as not encrypted when in fact they are):

    • Compressed files are extremely similar to some forms of encryption. Depending on how files are compressed, and how they are subsequently encrypted, they both look like very random byte streams so their histograms would look very similar. This becomes quite an issue when you consider that nowadays programs like MS OFFICE compresses it’s files (DOCX, XLSX, etc…). Depending on the encryption scheme used, it’s hard to tell them apart using just statistics

    In this series of tests we used openssl to encrypt our files — just as a malware author might do. However, there is more than one way to encrypt a file. You could:

    • Use a popular tool like AxCrypt. The encryption and compression used by this program makes it harder to tell files apart statistically speaking. During testing we found similar behavior with some other compression / encryption programs

    Since we try to make our anomaly detection systems as robust as possible, we added another layer of anomaly detection that doesn’t depend on file statistics. In the next blog post we’ll explore the operating system features that we monitor in order to detect (and block) suspect activity such as WannaCry or other ransomware.


    Interested in trying out RansomSift or CyberSift for enterprise? Contact Us!

    https://goo.gl/forms/7lV7CVdcX7kk9TYz1

    https://docs.google.com/forms/d/e/1FAIpQLSdCpjTC0QDjO4tnqfxgOH6pAux3ZfKubfS94DYfDG7zyOO8sg/viewform?usp=sf_link


    References

    [1] Scaife, N., Carter, H., Traynor, P. and Butler, K.R., 2016, June. Cryptolock (and drop it): stopping ransomware attacks on user data. In Distributed Computing Systems (ICDCS), 2016 IEEE 36th International Conference on (pp. 303–312). IEEE.

    [2] Mbol, F., Robert, J.M. and Sadighian, A., 2016, November. An efficient approach to detect torrentlocker ransomware in computer systems. In International Conference on Cryptology and Network Security (pp. 532–541). Springer International Publishing.


    Code

    The code used to generate the above statistics can be found below (written in GOLANG v1.8). Please note this is not the actual RansomSift code.

    https://gist.github.com/dvas0004/ebb2d40694cb7eeb1d9df1c1cac6f690

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  • David Vassallo 12:37 pm on April 7, 2017 Permalink  

    Using Technology to enable the creation of an open & decentralized government 

    Catch my recent guest blog post about using PKI, blockchain, Tor and cryptography to enable decentralized, transparent voting – supporting an open and honest government

     
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