WannaCry ransomware’s outbreak during the weekend was mitigated by having its kill switch domain registered. It was only a matter of time, however, for other cybercriminals to follow suit. Case in point: the emergence of UIWIX ransomware (detected by Trend Micro as RANSOM_UIWIX.A) and one notable Trojan our sensors detected.
Read More Earlier this year, two separate security risks were brought to light: CVE-2017-0144, a vulnerability in the SMB Server that could allow remote code execution that was fixed in March, and WannaCry/Wcry, a relatively new ransomware family that spread via Dropbox URLs in late April. These two threats have now been combined, resulting in one of the most serious ransomware attacks to hit users across the globe.
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A new Internet of Things (IoT) botnet called Persirai has been discovered targeting over 1,000 Internet Protocol (IP) Camera models based on various Original Equipment Manufacturer (OEM) products. This development comes on the heels of Mirai—an open-source backdoor malware that caused some of the most notable incidents of 2016 via Distributed Denial-of-Service (DDoS) attacks that compromised IoT devices such as Digital Video Recorders (DVRs) and CCTV cameras—as well as the Hajime botnet.
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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.
Read More by Jeanne Jocson and Jennifer Gumban Linux has long been the preferred operating system for enterprise platforms and Internet of Things (IoT) manufacturers. Linux-based devices are continually being deployed in smart systems across many different industries, with IoT gateways facilitating connected solutions and services central to different businesses. In connection to their widespread use, we’ve…
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