A method and system of detecting a malicious and/or botnet-related domain name, comprising: reviewing a domain name used in Domain Name System (DNS) traffic in a network; searching for information about the domain name, the information related to: information about the domain name in a domain name white list and/or a domain name suspicious list; and information about the domain name using an Internet search engine, wherein the Internet search engine determines if there are no search results or search results with a link to at least one malware analysis site; and designating the domain name as malicious and/or botnet-related based on the information

Method and system for detecting malicious and/or botnet-related domain names

System and method for detecting a domain generation algorithm (DGA), comprising: performing processing associated with clustering, utilizing a name-based features clustering module accessing information from an electronic database of NX domain information, the randomly generated domain names based on the similarity in the make-up of the randomly generated domain names; performing processing associated with clustering, utilizing a graph clustering module, the randomly generated domain names based on the groups of assets that queried the randomly generated domain names; performing processing associated with determining, utilizing a daily clustering correlation module and a temporal clustering correlation module, which clustered randomly generated domain names are highly con-elated in daily use and in time; and performing processing associated with determining the DGA that generated the clustered randomly generated domain names.

Method and system for detecting dga-based malware

A computerized system and method for performing behavioral clustering of malware samples, comprising: executing malware samples in a controlled computer environment for a predetermined time to obtain HTTP traffic; clustering the malware samples into at least one cluster based on network behavioral information from the HTTP traffic; and extracting, using the at least one processor, network signatures from the HTTP traffic information for each cluster, the network signatures being indicative of malware infection.

Method and system for network-based detecting of malware from behavioral clustering

Systems and methods for providing user interfaces are disclosed. In certain embodiments, a menu having a number of icons can be provided on a display device such that the icons are arranged around an initial cursor position, or an area that is touched by a user's finger or stylus, for example. Due to the icons being arranged around the initial cursor position, any one of the icons from the menu can be chosen with relatively small cursor movement. In certain embodiments, the menu can be divided into regions that overlap with the icons, such that cursor movement from the initial cursor position into a given region has a similar effect as movement into the corresponding icon itself (without actually moving the cursor onto the desired icon).

User interface systems and methods

A system and method of analysis. NX domain names are collected from an asset in a real network. The NX domain names are domain names that are not registered. The real network NX domain names are utilized to create testing vectors. The testing vectors are classified as benign vectors or malicious vectors based on training vectors. The asset is then classified as infected if the NX testing vector created from the real network NX domain names is classified as a malicious vector.

Method and system for detecting malware

This invention utilizes a number of Computational Intelligence (CI) techniques with different learning methods in a computer-aided detection, review and diagnosis (CAD) device. Specifically, an unsupervised learning method is used for clustering of types of abnormal findings. Then a number of classifiers for each type of findings are trained with appropriate learning algorithms; and combined in three different manners to produce one classifier that can be operated at three different operating points. A fuzzy system is used for mapping the findings to diagnostic reports constructed using a formal language. Finally, the finding statistics is calculated based on Bayesian probability. During image review, the device provides the readers some insight as to how it derives its outputs. The output of the device can be updated in an interactive and progressive manner by a human reader (radiologist). The output from classification can be updated by the human, and is fed as input to the assessment task. Again the output from assessment can be updated by the human reader, and is fed as input for the machine to produce statistical information. If so configured, the interactive information can be added to an online database so that the device can adapt its future behavior based on the new information.

Combination machine learning algorithms for computer-aided detection, review and diagnosis

A method for supporting diagnostic workflow from a medical imaging apparatus. A set of at least two images are obtained from a patient and displayed according to a user-specified image display layout selected from a plurality of image display layouts. One or more markers are associated with a region of interest in the displayed images. A list of regions of interest is generated, each having an entry for each associated marker. A classification is assigned to each entry in the list of regions of interest according to health risk.

Digital mammography system with improved workflow

A method and apparatus are disclosed for an image preprocessing device that automatically detects chestwall laterality; removes border artifacts; and segments breast tissue and pectoral muscle from digital mammograms. The algorithms in the preprocessing device utilize the computer cache, a vertical Sobel filter and a probabilistic Hough transform to detect curved edges. The preprocessing result, along with a pseudo-modality normalized image, can be used as input to a CAD (computer-aided detection) server or to a mammography image review workstation. In the case of workstation input, the preprocessing results improve the protocol for chestwall-to-chestwall image hanging, and support optimal image contrast display of each segmented region.

Fast preprocessing algorithms for digital mammography CAD and workstation

The present invention provides a method and system using computer-aided detection (CAD) algorithms to aid diagnosis and visualization of tomosynthesis mammography data. The proposed CAD algorithms process two-dimensional and three-dimensional tomosynthesis mammography images and identify regions of interest in breasts. The CAD algorithms include the steps of preprocessing; candidate detection of potential regions of interest; and classification of each region of interest to aid reading by radiologists. The detection of potential regions of interest utilizes two dimensional projection images for generating candidates. The resultant candidates in two dimensional images are back-projected into the three dimensional volume images. The feature extraction for classification operates in the three dimensional image in the neighborhood of the back-projected candidate location. The forward-projection and back-projection algorithms are used for visualization of the tomosynthesis mammography data in a fashion of synchronized MPR and VR.

Computer-aided diagnosis and visualization of tomosynthesis mammography data

The present invention provides a method for selecting mass density candidates from digital image, for example mammograms, for computer-aided lesion detection, review and diagnosis. A method of selecting mass density candidates from a digital image for computer-aided cancer detection, review and diagnosis includes down-sampling the digital image to a low resolution; smoothing an edge along a skinline; applying a Gaussian difference filter to enhance intensity to form a filtered image; masking the filtered image using a breast mask; using a Canny detector to find potential mass density contours; and generating a mass density candidate list from Canny contours produced in the Canny detector.

Patrick Bernard Heffernan

Papers

Combination machine learning algorithms for computer-aided detection, review and diagnosis

Digital mammography system with improved workflow

Fast preprocessing algorithms for digital mammography CAD and workstation

Computer-aided diagnosis and visualization of tomosynthesis mammography data

Algorithms for selecting mass density candidates from digital mammograms