Microsoft Technology Licensing, LLC

Fortinet, Inc.

John Payyappillil John

Yijou Chen

Fang Yu

Martin Abadi

Yinglian Xie

Theodore M Reed

Sowmya A Karmali

Xiaofei Guo

Matti A Vanninen

Nolan K Karpinski

Carsten Varming

Arvind Krishnamurthy

Mary L Singh

Nicholas W Mcnutt

Christopher Hall

An illustrative method includes identifying, based on static workload data associated with a compute environment, one or more attack paths from a network to one or more datasets associated with an entity, accessing runtime workload data associated with the compute environment, and performing, based on the runtime workload data, a risk mitigation operation associated with the one or more attack paths.

Attack path risk mitigation by a data platform using static and runtime data

An illustrative method includes scanning a compute environment associated with an entity and identifying one or more attack paths from a network to one or more datasets associated with the entity. The one or more attack paths each include a series of risk artifacts within the compute environment that can be exploited by an attacker to access the one or more datasets. The method further includes generating one or more attack path risk scores associated with the one or more attack paths and indicative of one or more levels of risk that the one or more attack paths could be exploited to access the one or more datasets. A risk mitigation operation associated with the one or more attack paths is performed based on the one or more attack path risk scores.

Attack path risk mitigation by a data platform

Search result poisoning attacks may be automatically detected by identifying groups of suspicious uniform resource locators (URLs) containing multiple keywords and exhibiting patterns that deviate from other URLs in the same domain without crawling and evaluating the actual contents of each web page. Suspicious websites are identified and lexical features are extracted for each such website. The websites are clustered based on their lexical features, and group analysis is performed on each group to identify at least one suspicious group. Other implementations are directed to detecting a search engine optimization (SEO) attack by processing a large population of URLs to identify suspicious URLs based on the presence of a subset of keywords in each URL and the relative newness of each URL.

Automatic detection of search results poisoning attacks

A framework identifies malicious queries contained in search logs to uncover relationships between the malicious queries and the potential attacks launched by attackers submitting the malicious queries. A small seed set of malicious queries may be used to identify an IP address in the search logs that submitted the malicious queries. The seed set may be expanded by examining all queries in the search logs submitted by the identified IP address. Regular expressions may be generated from the expanded set of queries and used for detecting yet new malicious queries. Upon identifying the malicious queries, the framework may be used to detect attacks on vulnerable websites, spamming attacks, and phishing attacks.

Identifying malicious queries

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John Payyappillil John