Abstract: Systems and methods for detecting suspicious malware by analyzing data such as transfer protocol data or logs from a host within an enterprise is provided. The systems and methods include a database for storing current data and historical data obtained from the network and a detection module and an optional display. The embodiments herein extract information from non-encrypted transfer protocol metadata, determine a plurality of features, utilize an outlier detection model that is based on historical behaviors, calculate a suspiciousness score, and create alerts for analysis by users when the score exceeds a threshold. In doing so, the systems and methods of the present invention improve the ability to identify suspicious outliers or potential malware on an iterative basis over time.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for detecting suspicious malware by analyzing data such as transfer protocol data or logs from a host within an enterprise is provided. The systems and methods include a database for storing current data and historical data obtained from the network and a detection module and an optional display. The embodiments herein extract information from non-encrypted transfer protocol metadata, determine a plurality of features, utilize an outlier detection model that is based on historical behaviors, calculate a suspiciousness score, and create alerts for analysis by users when the score exceeds a threshold. In doing so, the systems and methods of the present invention improve the ability to identify suspicious outliers or potential malware on an iterative basis over time.

Abstract: 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.

Abstract: Systems and methods for detecting suspicious malware by analyzing data such as transfer protocol data or logs from a host within an enterprise is provided. The systems and methods include a database for storing current data and historical data obtained from the network and a detection module and an optional display. The embodiments herein extract information from non-encrypted transfer protocol metadata, determine a plurality of features, utilize an outlier detection model that is based on historical behaviors, calculate a suspiciousness score, and create alerts for analysis by users when the score exceeds a threshold. In doing so, the systems and methods of the present invention improve the ability to identify suspicious outliers or potential malware on an iterative basis over time.