12440 results about "Network data" patented technology

A networking database containing a plurality of records for different identities in which identities are connected to one another by defined or interpreted Inter-Personal and Intra-Personal relationships. Individuals using the system may define, group and categorize specific identities and relationships; the system may also define, categorize and group both identities and relationships belonging to individuals registered with the system and unregistered users through computational analysis. Identities and relationships may be discovered by the system via an opt-in user-provided mechanism, via a third-party providing information, or through the system's own discovery. Identity and Relationship data can then be used to customize content.

A method and apparatus for selecting and converting database records or sets of related documents into network data and presenting that data in a network visualization system that enables users to select among, and move between various network displays by selecting one or more attributes of the data to be represented as the nodes and links of the network.

A system and method of interactive and live computerized matching in which a first user is provided with a personal data device including a wireless communication system. A secure network is configured to include a server programmed with a personal characteristic database and a database searching algorithm, and a wireless communication system at a specific geographic location that is capable of communicating with the user's personal data device. The first user is allowed to log on through the wireless communication system to the secure network using his or her personal data device, and communicates to and stores in the database personal data defining his or her characteristics and those for his or her ideal match. Either the secure network automatically identifies the first users precise geographic location, or the user himself otherwise indicates and stores in the database of the network an identifying address that indicates his current specific geographic location. The user then instructs the database searching algorithm to search for and identify other users similarly logged on to the network at the same specific geographic location. The user is able to initiate a search of the database for possible matches within the pool of other users also logged on to the network at the same specific geographic location. The secure network then indicates to the first user the results of the search.

Exemplary systems and methods for malware attack detection and identification are provided. A malware detection and identification system can comprise a controller. The controller can comprise an analysis environment configured to transmit network data to a virtual machine, flag input values associated with the network data from untrusted sources, monitor the flagged input values within the virtual machine, identify an outcome of one or more instructions that manipulate the flagged input values, and determine if the outcome of the one or more instructions comprise an unauthorized activity.

Embodiments of the invention address the problem of detecting bots in network traffic based on a classification model learned during a training phase using machine learning algorithms based on features extracted from network data associated with either known malicious or known non-malicious client and applying the learned classification model to features extracted in real-time from current network data. The features represent communication activities between the known malicious or known non-malicious client and a number of servers in the network.

A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the connectivity graph based feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the connectivity graph based feature, and categorizing the data unit as associated with the botnet based on the label.

A method of gathering information relevant to the interests of a user includes receiving activity history associated with the user from a network data source, where the activity history includes a description of a user action initiated by the user, generating a first metadata item based upon the activity history; and storing the first metadata item as user profile information associated with the user in a user profile database. Generating the first metadata item may include extracting text from the activity history and generating the first metadata item based upon the text. The activity history may include a description of a user action. The method may further include providing a server plugin for performing a second method in response to access of web content on a server by the user, the second method comprising retrieving a content object from the server, determining if the first metadata item is relevant to the content object, adding a web link to the web content as a reference to the content object in response to the content object being relevant to the first metadata item; and providing the web content to the user.

Data is now able to be transferred back and forth between computer systems which have disparate file systems, e.g., between a computer having a hierarchical file system and a computer having a nonhierarchical file system. The differences, however, are not limited to the file storage system. Data may now be transferred across different platforms, different human and computer languages, different binary codes, etc. From one computer system the associated metadata is stored in a interpretable format on the second computer system so that when data and its associated metadata are transferred, the second computer system can interpret the transferred data. Data transfer can be accomplished in both directions. Thus, a tool writer or a software developer using a integrated development environment, such as Eclipse, on a Linux workstation with its hierarchical file system may download source files and other resources from an iSeries computer server with its OS/400 nonhierarchical file system, modify the resources, and then put them back onto the iSeries machine for use on the iSeries. The metadata of the source file and other resources are stored in a .properties file which, if in XML format, supports the transfer of resources across the Internet.