Ironnet Cybersecurity, Inc.

Ania Kacewicz

Christopher S Stinson

Matthew C Swort

Systems and methods for detecting anomalous and malicious URL's by analyzing markup language structure, such as HTML, are provided. The systems and methods include the querying of a URL to obtain the markup language data. The markup language data their corresponding elements and their locations rows/depths are parsed into coordinates within a 2-dimensional grid and then processed into features. A color is assigned to each feature as a function of the type of feature. The three dimensions (x, y coordinates and color coordinate) of the features are used to generate an image. The generated images are then compressed to facilitate processing. The compressed images of common websites are analyzed using deep machine learning algorithms to generate a model that represents their structure. These generated models are then used to detect suspicious and/or anomalous websites.

Systems and methods of detecting anomalous websites

Systems and techniques for detecting advertising fraudulent traffic, or invalid traffic, by correlating advertising traffic with cyber network defense events are described. For example, described techniques include querying cyber network traffic events, querying the metadata returned by the tag script placed in the displayed advertisement, and correlating times, internet protocol (IP) addresses, publisher domains, and referrer domains with domains and IP addresses flagged by network cyber security events.

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Baltimore, MD, United States of America

Ania Kacewicz