We noticed a Linux coin miner with scripts almost the same as KORKERDS, and with just one crontab removes other miners and malware installed in the system upon infection.Read More
We found a new Mirai variant we’ve called Yowai and Gafgyt variant Hakai abusing a ThinkPHP flaw for propagation and DDoS attacks.Read More
August’s Android Security Bulletin includes three file system vulnerabilities (CVE-2017-10663, CVE-2017-10662, and CVE-2017-0750 that were discovered by Trend Micro researchers. These vulnerabilities could cause memory corruption on the affected devices, leading to code execution in the kernel context. This would allow for more data to be accessed and controlled by the malware. A malicious app could be used to trigger this vulnerability, which occurs when a malicious disk using the F2FS (Flash-Friendly File System) is mounted. The disk can either be an actual physical device or a virtual file image.Read More
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