In the last weeks of October, the Microsoft promotes digital security efforts as part of National Cyber Security Awareness Month Cybersecurity Awareness Month or 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 machine learning-based algorithm that detects attacks code spraying access (password spray attacks) with significantly improved performance from its previous mechanism.
For those who do not know, attack password spray is a relatively crude and common form of online attack in which a malicious user attacks thousands of IPs with a few commonly used passwords instead 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 hacking attacks, Microsoft has created a mechanism that recognizes "the system's major στο worldwide traffic failure" and alerts endangered organizations. Today the company has improved this mechanism by training a new machine learning algorithm that uses features such as IP reputation, unknown login properties and other account divergences to detect when someone is being attacked by password spraying.
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.
