deepblue/windows-itpro-docs
1
0
Fork 0
mirror of https://github.com/MicrosoftDocs/windows-itpro-docs.git synced 2025-05-14 06:17:22 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Update behavioral-blocking-containment.md

Browse Source
This commit is contained in:
Denise Vangel-MSFT 2020-05-20 14:02:23 -07:00
parent 221a600cde
commit f54563fcf3
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
windows/security/threat-protection/microsoft-defender-atp/behavioral-blocking-containment.md
View File

@ -67,7 +67,7 @@ Expect more to come in the area of behavioral blocking and containment, as Micro
As described in [In hot pursuit of elusive threats: AI-driven behavior-based blocking stops attacks in their tracks](https://www.microsoft.com/security/blog/2019/10/08/in-hot-pursuit-of-elusive-threats-ai-driven-behavior-based-blocking-stops-attacks-in-their-tracks), a credential theft attack against 100 organizations around the world was stopped by behavioral blocking and containment capabilities. Spear-phishing email messages that contained a lure document were sent to the targeted organizations. If a recipient opened the attachment, a related remote document was able to execute code on the user’s device and load Lokibot malware, which stole credentials, exfiltrated stolen data, and waited for further instructions from a command-and-control server. As described in [In hot pursuit of elusive threats: AI-driven behavior-based blocking stops attacks in their tracks](https://www.microsoft.com/security/blog/2019/10/08/in-hot-pursuit-of-elusive-threats-ai-driven-behavior-based-blocking-stops-attacks-in-their-tracks), a credential theft attack against 100 organizations around the world was stopped by behavioral blocking and containment capabilities. Spear-phishing email messages that contained a lure document were sent to the targeted organizations. If a recipient opened the attachment, a related remote document was able to execute code on the user’s device and load Lokibot malware, which stole credentials, exfiltrated stolen data, and waited for further instructions from a command-and-control server.
Behavior-based machine learning models in Microsoft Defender ATP caught and stopped the attacker’s techniques at two points in the attack chain: Behavior-based machine learning models in Microsoft Defender ATP caught and stopped the attacker’s techniques at two points in the attack chain:
- The first protection layer detected exploit behavior. Machine learning classifiers in the cloud correctly identified the threat as and immediately instructed the client device to block the attack. - The first protection layer detected the exploit behavior. Machine learning classifiers in the cloud correctly identified the threat as and immediately instructed the client device to block the attack.
- The second protection layer, which helped stop cases where the attack got past the first layer, detected process hollowing, stopped that process, and removed the corresponding files (such as Lokibot). - The second protection layer, which helped stop cases where the attack got past the first layer, detected process hollowing, stopped that process, and removed the corresponding files (such as Lokibot).
While the attack was detected and stopped, alerts, such as an "initial access alert," were triggered and appeared in the Microsoft Defender Security Center ([https://securitycenter.windows.com](https://securitycenter.windows.com)): While the attack was detected and stopped, alerts, such as an "initial access alert," were triggered and appeared in the Microsoft Defender Security Center ([https://securitycenter.windows.com](https://securitycenter.windows.com)):