![]() While R-CNNs tend to be very accurate, the biggest problem with the R-CNN family of networks is their speed - they were incredibly slow, obtaining only 5 FPS on a GPU. The outputs of the RPNs are then passed into the R-CNN component for final classification and labeling. ![]() It wasn’t until Girshick et al.’s follow-up 2015 paper, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, that R-CNNs became a true end-to-end deep learning object detector by removing the Selective Search requirement and instead relying on a Region Proposal Network (RPN) that is (1) fully convolutional and (2) can predict the object bounding boxes and “objectness” scores (i.e., a score quantifying how likely it is a region of an image may contain an image). The Fast R-CNN algorithm made considerable improvements to the original R-CNN, namely increasing accuracy and reducing the time it took to perform a forward pass however, the model still relied on an external region proposal algorithm. published a second paper in 2015, entitled Fast R- CNN. The problem with the standard R-CNN method was that it was painfully slow and not a complete end-to-end object detector. These regions were then passed into a CNN for classification, ultimately leading to one of the first deep learning-based object detectors.proposed an object detector that required an algorithm such as Selective Search (or equivalent) to propose candidate bounding boxes that could contain objects. In the first R-CNN publication, Rich feature hierarchies for accurate object detection and semantic segmentation, (2013) Girshick et al.R-CNNs are one of the first deep learning-based object detectors and are an example of a two-stage detector. ![]() R-CNN and their variants, including the original R-CNN, Fast R- CNN, and Faster R-CNN.When it comes to deep learning-based object detection, there are three primary object detectors you’ll encounter: We’ll use YOLO with OpenCV in this blog post. Figure 1: A simplified illustration of the YOLO object detector pipeline ( source). The IOCs are available in the SentinelOne OSAMiner report, here. “In this case, we have not seen the actor use any of the more powerful features of AppleScript that we’ve discussed elsewhere, but that is an attack vector that remains wide open and which many defensive tools are not equipped to handle.” “Run-only AppleScripts are surprisingly rare in the macOS malware world, but both the longevity of and the lack of attention to the macOS.OSAMiner campaign, which has likely been running for at least 5 years, shows exactly how powerful run-only AppleScripts can be for evasion and anti-analysis,” Stokes concluded in his report yesterday. Stokes and the SentinelOne team hope that by finally cracking the mystery surrounding this campaign and by publishing IOCs, other macOS security software providers would now be able to detect OSAMiner attacks and help protect macOS users. Yesterday, Stokes published the full-chain of this attack, along with indicators of compromise (IOCs) of past and newer OSAMiner campaigns. Since “run-only” AppleScript come in a compiled state where the source code isn’t human-readable, this made analysis harder for security researchers. The primary reason was that security researchers weren’t able to retrieve the malware’s entire code at the time, which used nested run-only AppleScript files to retrieve its malicious code across different stages.Īs users installed the pirated software, the boobytrapped installers would download and run a run-only AppleScript, which would download and run a second run-only AppleScript, and then another final third run-only AppleScript. SentinelOne said that two Chinese security firms spotted and analyzed older versions of the OSAMiner in August and September 2018, respectively.īut their reports only scratched the surface of what OSAMiner was capable of, SentinelOne macOS malware researcher Phil Stokes said yesterday. Nested run-only AppleScripts, for the win!īut the cryptominer did not go entirely unnoticed. “From what data we have it appears to be mostly targeted at Chineses/Asia-Pacific communities,” the spokesperson added. “OSAMiner has been active for a long time and has evolved in recent months,” a SentinelOne spokesperson told ZDNet in an email interview on Monday. Named OSAMiner, the malware has been distributed in the wild since at least 2015 disguised in pirated (cracked) games and software such as League of Legends and Microsoft Office for Mac, security firm SentinelOne said in a report published this week. For more than five years, macOS users have been the targets of a sneaky malware operation that used a clever trick to avoid detection and hijacked the hardware resources of infected users to mine cryptocurrency behind their backs.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |