Over the last year, the number of mobile phones overtook the world population. In countries like the United States, mobile subscribers outnumbered traditional landline users and half of Americans shifted to mobile-only to communicate. In modern smart cities, wireless-only buildings are becoming the new construction standard for homes, factories, and organizations in general. Landline phones are going away—sooner rather than later.
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Locality Sensitive Hashing (LSH) is an algorithm known for enabling scalable, approximate nearest neighbor search of objects. LSH enables a precomputation of a hash that can be quickly compared with another hash to ascertain their similarity. A practical application of LSH would be to employ it to optimize data processing and analysis. An example is transportation company Uber, which implemented LSH in the infrastructure that handles much of its data to identify trips with overlapping routes and reduce inconsistencies in GPS data. Trend Micro has been actively researching and publishing reports in this field since 2009. In 2013, we open sourced an implementation of LSH suitable for security solutions: Trend Micro Locality Sensitive Hashing (TLSH).
TLSH is an approach to LSH, a kind of fuzzy hashing that can be employed in machine learning extensions of whitelisting. TLSH can generate hash values which can then be analyzed for similarities. TLSH helps determine if the file is safe to be run on the system based on its similarity to known, legitimate files. Thousands of hashes of different versions of a single application, for instance, can be sorted through and streamlined for comparison and further analysis. Metadata, such as certificates, can then be utilized to confirm if the file is legitimate.
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Microsoft Internet Information Services (IIS) 6.0 is vulnerable to a zero-day Buffer Overflow vulnerability (CVE-2017-7269) due to an improper validation of an ‘IF’ header in a PROPFIND request.
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CERBER is a ransomware family which has adopted a new technique to make itself harder to detect: it is now using a new loader which appears to be designed to evade detection by machine learning solutions. This loader is designed to hollow out a normal process where the code of CERBER is instead run.
Read More Part of this month’s Patch Tuesday is an update for a zero-day information disclosure vulnerability (CVE-2017-0022), which we privately reported to Microsoft in September 2016. This vulnerability was used in the AdGholas malvertising campaign and later integrated into the Neutrino exploit kit.
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