Abstract: A mesh network of interconnected wireless nodes in which each node independently manages a wireless connection to one or more other wireless nodes for transporting data, and stochastically refreshes and maintains internode connections in the wireless mesh network. A network overlay orchestrator in each node periodically validates the connections to other nodes in the mesh network based on a current topology of connected nodes to ensure the efficiency of current connections. Connection logic replaces, if a connection validation fails, the failed connection with a newly established connection from a set of available neighbor nodes, and replaces, if none of the current connections fail validation, a stochastically selected connection with a newly established connection from the set of available neighbor nodes for promoting perturbation in the current internode connections.

Abstract: A detection device for identification and isolation of unauthorized skimmer/shimmer devices takes the form of a portable electronics package adapted for deployment under or near a point-of-sale (POS) station that may be targeted by such skimmer. The detection device is intended for placement near or adjacent an electronic exchange of personal, financial, and/or sensitive information from a payment card, mobile device, or similar magnetic, optical, or radio frequency medium. Unscrupulous interception devices periodically transmit gathered information for reception. The detection device monitors transmissions for those having characteristics indicative of the unscrupulously gathered information, and renders an output signal alerting to the presence and location of an illicit capture device.

Abstract: A biometric attribution approach identifies a keyboard actor based on timing between entered keystrokes. Patterns tend to emerge in a timing interval between keystrokes entered by an actor. The keystroke patterns of an actor are analyzed to compute a signature exhibited by the actor. Gathered or intercepted keystroke patterns of an unknown actor are compared to identify a likelihood that typing sessions emanated from a common actor. Keystroke activity of a purported suspect actor can be compared to a database or model of keystroke attributes for determining if the keystroke activity emanated from the same actor as other keystroke sequences. Keystroke patterns rely only on the timing between keystrokes, as key data and upstroke information need not be gathered since the comparisons reply only on keystroke timing deltas.

Abstract: One example method of operation may include identifying event block allocations of one or more of data memory and data storage allocations, assigning indicators to the event block allocations based one or more determined statuses associated with the event block allocations, populating a display interface with the event block allocations and the indicators, performing a trace event of the event block allocations, identifying a target event block allocation among the event block allocations, and creating a notification identifying an allocation violation based on the identified target event block allocation.

Abstract: A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics (“gragnostics”) computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.

Abstract: One example method of operation may include creating a force approximation of a number of nodes in a defined space at an initial time (t0), the force approximation being based on a data realization simulation model of an n-body simulation, where n is an integer greater than one. The method may also include determining initial displacement changes of one or more of the nodes within the defined space has occurred in the force approximation, summing the initial displacement changes of the one or more of the nodes to create a summed total displacement, creating an initial displacement threshold (Td) based on the summed total displacement.

Abstract: A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics (“gragnostics”) computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.