Microsoft Technology Licensing, LLC

Ely Abramovitch

Hani Hana Neuvirth

Moshe Israel

Timothy William Burrell

Yaakov Garyani

Shany Klein Antman

Diana Attar-Sityon

Ishai Wertheimer

Amir Keren

Yaron David Fruchtmann

According to examples, an apparatus may include a processor and a memory on which are stored machine-readable instructions that, when executed by the processor, may cause the processor to receive event data for a subject incident. The processor may filter a set of candidate incidents to identify a first predefined number of candidate incidents. The first predefined number of candidate incidents may be filtered based on a respective first similarity score assigned to each of the candidate incidents. The processor may assign a respective second similarity score to each of the identified first predefined number of candidate incidents. The second similarity score may be based on common property values between the subject incident and respective candidate incidents. The processor may identify and output a second predefined number of candidate incidents among the first predefined number of candidate incidents based on the assigned second similarity score.

Identification of similar incidents based on similarity scores

The principles described herein relate to the training and implementation of a model designed to estimate the probability of new security incidents being true incidents. This occurs in an environment where a service such as a SIEM monitors a network of computing systems and other resources and detects a variety of incidents that could be security threats. These incidents are reported to the SOC for investigation and the SOC will take appropriate action to mitigate potential threats of true security breaches. As part of the investigation process, the SOC can label whether a security incident is true, false or benign. After labeling enough security incidents a model can be produced to estimate the probability that new security incidents are true incidents. This would help the SOC filter through security incidents more efficiently and allow for quicker response of the most likely security breaches.

Likelihood assessment for security incident alerts

Embodiments detect cyberattack campaigns against multiple cloud tenants by analyzing activity data to find sharing anomalies. Data that appears benign in a single tenant's activities may indicate an attack when the same or similar data is also found for additional tenants. Attack detection may depend on activity time frames, on how similar certain activities of different tenants are to one another, on how unusual it is for different tenants to share an activity, and on other factors. Sharing anomaly analysis may utilize hypergeometric probabilities or other statistical measures. Detection avoidance attempts using entity randomization are revealed and thwarted. Authorized vendors may be recognized, mooting anomalousness. Although data from multiple tenants is analyzed together for sharing anomalies while monitoring for attacks, tenant confidentiality and privacy are respected through technical and legal mechanisms. Mitigation is performed in response to an attack indication.

Multitenant sharing anomaly cyberattack campaign detection

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Ely Abramovitch