Microsoft Technology Licensing, LLC

Bugra Karabey

Priyatham Reddy Allala

Tvisha Rajesh Gangwani

Andre Oneal Alfred

Jing Han Shiau

Sasikumar Natarajan

Meenal Srivastva

Muhammad Ali Siddiqui

Houtan Faridi

Subramanian Nagarajan

Generally discussed herein are devices, systems, and methods for cyber attack response. A method can include receiving, at a compute device, a natural language description of activity on a computer network, executing, based on the natural language description, a natural language processing (NLP) model to provide a tactic and technique of a cyber attack associated with the natural language description, determining, based on the provided tactic and technique, a response to mitigate the cyber attack, and implementing the determined response on the computer network.

NLP based identification of cyberattack classifications

Embodiments provide for the self-registration and automatic inventorying of enterprise Internet-of-Things (IoT) devices in an enterprise environment. An asset inventory agent on a network among many networks (i.e., within the enterprise environment) can receive a data packet from a computing device. The data packet is communicated via the network shared between the asset inventory agent and the computing device. The asset inventory agent can analyze the data packet, which can reveal a communication protocol associated with the computing device. A discovery rule associated with the protocol can be selected to extract inventory information from the data packet. The inventory information can be utilized to generate a distributed ledger transaction, which is communicated to a set of nodes adapted to collectively store the inventory information associated with the computing device onto the distributed ledger.

Blockchain and cloud connectivity-based self-registration and inventorying of enterprise IoT assets

Disclosed herein is a system for minimizing the amount of time it takes to process and understand an unstructured, text-heavy document that includes important security-related information. The system uses a model that implements natural language processing techniques to semantically analyze an unstructured, text-heavy document, in order to identify notable security features useable to resolve the security issues in a more effective and efficient manner (e.g., without the need for a technical security professional). More specifically, the system receives an unstructured document that includes text which may be provided by multiple different security providers. The system is configured to apply the model to various text segments (e.g., a phrase, a sentence, a paragraph, etc.) included in the unstructured document to identify notable security features. The model can then associate the notable security features with a prescribed set of mitigation actions that can be implemented to resolve a security issue.

Automated contextual understanding of unstructured security documents

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Redmond, WA, United States of America

Bugra Karabey