The security industry as a whole loves collecting data, and researchers are no different. With more data, they commonly become more confident in their statements about a threat. However, large volumes of data require more processing resources, as extracting meaningful and useful information from highly unstructured data is particularly difficult. As a result, manual data analysis is often the only choice, forcing security professionals like investigators, penetration testers, reverse engineers, and analysts to process data through tedious and repetitive operations.Read More
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.Read More
Some time ago, I was asked by a colleague to develop a set of Yara rules to detect samples of the Stampado ransomware family. (Yara is an open-source tool used by security researchers to spot and categorize malware samples according to a set of defined rules.)
Stampado is a relatively new Ransomware-as-a-Service (RaaS) threat that’s been on our radar recently. I had access to only a few samples at the time, and first tried looking for common strings among them but had no luck. I then went to compare the files structures and realized all of them had an interesting section at the end of the file, like the one starting at offset 0xde000 as follows:Read More
Researchers, whether independent or from security vendors, have a responsibility to properly disseminate the information they gathered to help the industry as well as users. Even with the best intentions, improper disclosure of sensitive information can lead to complicated, and sometimes even troublesome scenarios.Read More
In August of this year, we presented at Blackhat our paper titled The GasPot Experiment: Unexamined Perils in Using Gas-Tank-Monitoring Systems. GasPot was a honeypot designed to mimic the behavior of the Guardian AST gas-tank-monitoring system. It was designed to look like no other existing honeypot, with each instance being unique to make fingerprinting by attackers impossible. These were deployed within networks located in various countries, to give us a complete picture of the attacks facing gas tank monitoring systems.Read More