automation – PossibleThreat Articles

Microsoft Application Inspector is a new source code analyzer that helps you understand what a program does by identifying interesting features and characteristics.

The post Introducing Microsoft Application Inspector appeared first on Microsoft Security .

Microsoft Security

Data science for cybersecurity: A probabilistic time series model for detecting RDP inbound brute force attacks

December 18, 2019 0 Comments AI and machine learning, automation, brute force attack, cybersecurity, data science, Endpoint security, event id 4625, Microsoft Defender Advanced Threat Protection, Microsoft security intelligence, microsoft threat experts, probablistic time series model, Remote Desktop Protocol (RDP), Security Intelligence, Threat protection, Windows Security

Credit to Author: Eric Avena| Date: Wed, 18 Dec 2019 18:00:24 +0000

Microsoft Defender ATP data scientists and threat hunters collaborate to use a data science-driven approach to detecting RDP brute force attacks to protect customers against real-world threats.

The post Data science for cybersecurity: A probabilistic time series model for detecting RDP inbound brute force attacks appeared first on Microsoft Security .

Microsoft Security

In hot pursuit of elusive threats: AI-driven behavior-based blocking stops attacks in their tracks

October 8, 2019 0 Comments AI and machine learning, amsi paired machine learning, automation, behavior-based machine learning, behavioral blocking and containment, cybersecurity, endpoint protection platform, Endpoint security, Microsoft Defender ATP, Microsoft security intelligence, Security Intelligence, threat and malware prevention

Credit to Author: Eric Avena| Date: Tue, 08 Oct 2019 15:00:11 +0000

Two new machine learning protection features within the behavioral blocking and containment capabilities in Microsoft Defender ATP specialize in detecting threats by analyzing behavior, adding new layers of protection after an attack has started running.

The post In hot pursuit of elusive threats: AI-driven behavior-based blocking stops attacks in their tracks appeared first on Microsoft Security .

Microsoft Security

Foundations of Flow—secure and compliant automation, part 2

September 11, 2019 0 Comments automation, Data governance, Threat protection

Credit to Author: Todd VanderArk| Date: Wed, 11 Sep 2019 16:00:28 +0000

In part 2 of our two-part series, we go deeper into how Microsoft Flow secures your users and data, as well as enhances the IT experience.

The post Foundations of Flow—secure and compliant automation, part 2 appeared first on Microsoft Security .

Microsoft Security

From unstructured data to actionable intelligence: Using machine learning for threat intelligence

August 8, 2019 0 Comments AI and machine learning, automation, cybersecurity, Endpoint security, indicators of compromise (IoC), Machine Learning, Microsoft Defender Advanced Threat Protection, Microsoft security intelligence, Security Intelligence, threat intelligence, TTPs, Windows Security

Credit to Author: Eric Avena| Date: Thu, 08 Aug 2019 16:30:12 +0000

Machine learning and natural language processing can automate the processing of unstructured text for insightful, actionable threat intelligence.

The post From unstructured data to actionable intelligence: Using machine learning for threat intelligence appeared first on Microsoft Security .

Microsoft Security

New machine learning model sifts through the good to unearth the bad in evasive malware

July 25, 2019 0 Comments AI and machine learning, Antivirus, automation, cybersecurity, detection evasion, Endpoint security, lockergoga, Machine Learning, Microsoft Defender Advanced Threat Protection, Microsoft Defender ATP, Microsoft security intelligence, monotonic models, next generation protection, Windows Defender Antivirus

Credit to Author: Eric Avena| Date: Thu, 25 Jul 2019 16:30:55 +0000

Most machine learning models are trained on a mix of malicious and clean features. Attackers routinely try to throw these models off balance by stuffing clean features into malware. Monotonic models are resistant against adversarial attacks because they are trained differently: they only look for malicious features. The magic is this: Attackers can’t evade a monotonic model by adding clean features. To evade a monotonic model, an attacker would have to remove malicious features.

The post New machine learning model sifts through the good to unearth the bad in evasive malware appeared first on Microsoft Security .

MalwareBytes Security

Caution: Misuse of security tools can turn against you

July 19, 2019 0 Comments automation, Carbon Black, data leaks, EDR platforms, exes, infosec, opsec, plugins, remediation and prevention, Researcher's corner, rules-based segmentation, security solutions, security tools, virustotal, VT

Credit to Author: Vasilios Hioureas| Date: Thu, 11 Jul 2019 17:34:57 +0000

If not implemented correctly, the very security tools we use to keep our information private may actually cause data leaks themselves. We outline a few cases and provide suggestions for researchers and security admins.

Categories:

Researcher’s corner

Tags: automation Carbon Black data leaks EDR platforms exes infosec opsec plugins remediation and prevention rules-based segmentation security solutions security tools virustotal VT