Small businesses targeted by highly localized Ursnif campaign
Cyber thieves are continuously looking for new ways to get people
to click on a bad link, open a malicious file, or install a poisoned
update in order to steal valuable data. In the past, they cast as wide a
net as possible to increase the pool of potential victims. But attacks
that create a lot of noise are often easier to spot and stop. Cyber
thieves are catching on that we are watching them, so they are trying
something different. Now we’re seeing a growing trend of small-scale,
localized attacks that use specially crafted social engineering to stay
under the radar and compromise more victims.
In social engineering attacks, is less really more?
A new malware campaign puts that to the test by targeting home users and small businesses in specific US cities. This was a focused, highly localized attack that aimed to steal sensitive info from just under 200 targets. Macro-laced documents masqueraded as statements from legitimate businesses. The documents are then distributed via email to target victims in cities where the businesses are located.
With Windows Defender AV’s next gen defense, however, the size of the attack doesn’t really matter.
Several cloud-based machine learning algorithms detected and blocked the malicious documents at the onset, stopping the attack and protecting customers from what would have been the payload, info-stealing malware Ursnif.
TO read the full article on Microsoft site go here
In social engineering attacks, is less really more?
A new malware campaign puts that to the test by targeting home users and small businesses in specific US cities. This was a focused, highly localized attack that aimed to steal sensitive info from just under 200 targets. Macro-laced documents masqueraded as statements from legitimate businesses. The documents are then distributed via email to target victims in cities where the businesses are located.
With Windows Defender AV’s next gen defense, however, the size of the attack doesn’t really matter.
Several cloud-based machine learning algorithms detected and blocked the malicious documents at the onset, stopping the attack and protecting customers from what would have been the payload, info-stealing malware Ursnif.
TO read the full article on Microsoft site go here
