In the last weeks of October, Microsoft is promoting its digital efforts better safetys as part of the observance of National Cyber Security Awareness Month (NCSAM).
The company announced new initiatives to promote cyber awareness, revealed the Zero Trust Deployment Center, released one Adversarial ML Threat Matrix and started a pretty successful attack against the malicious botnet Trickbot.
Now the company said it has developed a new algorithm based on mechanics learning that detects attacks password spray attacks with significantly improved performance from its previous mechanism.
For those who don't know, a password spray attack is a relatively crude and common form of cyber attack in which a malicious user attacks thousands of IPs with a few commonly used codeinstead of trying multiple passwords on a single user.
Although the success rate per account is not impressive enough, the attack is very difficult to detect.
To combat password injection attacks, Microsoft created a mechanism that recognizes “the basic system failure in the … worldwide traffic” and notifies the organizations at risk. Today the company has improved this mechanism by training a new machine learning algorithm that uses features such as IP reputation, unknown connection properties and other account anomalies to detect when someone is being subjected to a password spraying attack.
Microsoft claims that its new model has a 100% increase in recall compared to the previous heuristic algorithm. This means that it detects twice the number of compromised accounts. In addition, it has 98% accuracy, which means that if the model claims that an account has been hacked by a password, then it is almost certainly true.
The new model will soon be available to Azure AD Identity Protection customers, who will be able to use it on the portal and the APIs they use to protect their identity.