Optimisation For Abnormal Deny Rate for Source IP
Hi, I have recently enabled the "Abnormal Deny Rate for Source IP" alert in Microsoft Sentinel and found it to be quite noisy, generating a large number of alerts many of which do not appear to be actionable. I understand that adjusting the learning period is one way to reduce this noise. However, I am wondering if there are any other optimisation strategies available that do not involve simply changing the learning window. Has anyone had success with tuning this rule using: Threshold-based suppression (e.g. minimum deny count)? Source IP allowlists? Frequency filters (e.g. repeated anomalies over multiple intervals)? Combining with other signal types before generating alerts? Open to any suggestions, experiences, or best practices that others may have found effective in reducing false positives while still maintaining visibility into meaningful anomalies. Thanks in advance,New Blog Post | Introduction to Machine Learning Notebooks in Microsoft Sentinel
Read the full blog post here: Introduction to Machine Learning Notebooks in Microsoft Sentinel It has never been harder to keep hybrid environments secure. Microsoft’s Security Research teams are observing an increasing number and complexity of cybercrimes occurring across all sectors of critical infrastructure, from targeted ransomware attacks to increasing password and phishing campaigns on email, according to the https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RWMFIi. The https://www.proofpoint.com/us/resources/threat-reports/cost-of-insider-threats#:~:text=As%20the%202022%20Cost%20of,a%20third%20to%20%2415.38%20million. reported that threat incidents have risen by over 44% in the last two years, with associated costs exceeding $15.38M per incident per year, up by a third in the preceding years. The report also concluded that there has been a 10.3% increase in the average time taken to contain an incident, from 77 days to 85 days. Advanced tools, techniques, and processes used by threat actor groups allow them to counter obsolete defences and scale their attack campaigns to a broad range of victims, from government organisations to for-profit enterprises. Original Post: New Blog Post | Introduction to Machine Learning Notebooks in Microsoft Sentinel - Microsoft Tech CommunityNew Blog Post | Microsoft Sentinel customizable machine learning based anomalies Generally Available
Microsoft Sentinel customizable machine learning based anomalies is Generally Available - Microsoft Tech Community Security analysts can use anomalies to reduce investigation and hunting time, as well as detect new and emerging threats. Typically, these benefits come at the cost of a high benign positive rate, but Microsoft Sentinel’s customizable anomaly models are tuned by our data science team and trained with the data in your Microsoft Sentinel workspace to reduce, providing out-of-the box value. If security analysts need to tune them further, the process is simple and requires no knowledge of machine learning. Read this blog to find out which capabilities were supported in Public Preview and how to tune anomalies: Democratize Machine Learning with Customizable ML Anomalies - Microsoft Tech Community In this blog, we will discuss how customizable machine learning based anomalies have improved since Public Preview. Original Post: New Blog Post | Microsoft Sentinel customizable machine learning based anomalies Generally Available - Microsoft Tech CommunityAzure Sentinel | Build-Your-Own Machine Learning Model
Microsoft's Azure Sentinel Build-Your-Own Machine Learning model (BYO ML) provides an ML threat detection platform, tools, and templates to accelerate customer-building ML detection for their unique business problems. More details are covered on the blogs as part of the https://youtu.be/QDIuvZbmUmc The free upcoming public webinar on the same topic is coming up on January 12. Registration at https://aka.ms/SecurityWebinars.
