Abstract: Blocking high-risk IP connections in real-time while allowing tailoring of an acceptable risk profile to match the security requirements of network resources. By acquiring IP threat information about IP addresses, including risk confidence levels, assigning weighting factor values corresponding to various characteristics of the IP addresses, and mathematically transforming the risk confidence levels using the weighting factor values, traffic from IP addresses posing unacceptable levels of risk is blocked. Further, mathematically transforming risk confidence level to a user-defined acceptable risk level permits allowing traffic from the IP addresses having an acceptable level of risk.

Abstract: Blocking high-risk IP connections in real-time while allowing tailoring of an acceptable risk profile to match the security requirements of network resources. By acquiring IP threat information about IP addresses, including risk confidence levels, assigning weighting factor values corresponding to various characteristics of the IP addresses, and mathematically transforming the risk confidence levels using the weighting factor values, traffic from IP addresses posing unacceptable levels of risk is blocked. Further, mathematically transforming risk confidence level to a user-defined acceptable risk level permits allowing traffic from the IP addresses having an acceptable level of risk.

Abstract: Blocking high-risk IP connections in real-time while allowing tailoring of an acceptable risk profile to match the security requirements of network resources. By acquiring IP threat information about IP addresses, including risk confidence levels, assigning weighting factor values corresponding to various characteristics of the IP addresses, and mathematically transforming the risk confidence levels using the weighting factor values, traffic from IP addresses posing unacceptable levels of risk is blocked. Further, mathematically transforming risk confidence level to a user-defined acceptable risk level permits allowing traffic from the IP addresses having an acceptable level of risk.

Abstract: A computer executed method is disclosed for sorting a plurality of internet protocol (IP) addresses. The method includes dividing the range of IP addresses into a plurality of clusters representing a plurality of contiguous sub-ranges, assigning each IP address to the cluster associated with the sub-range that includes that IP address, and assigning the IP addresses in each cluster to one of a plurality of pages. If one of the pages has a size less than a page size limit, the method includes duplicating on that page at least one of the IP addresses assigned to that page. For each page, the IP addresses assigned to that page are ordered by numeric value. A network appliance incorporating aspects of the method is also disclosed.

Abstract: A method for determining a flow of data is described. The method includes determining whether a packet including a first number of at least one bit within a first set is assigned a classification value based on the at least one bit within the first set and determining a result to be applied to the packet upon determining that the packet is assigned the classification value. The method further includes analyzing, by a processor, at least one bit of a second number within a second set of the packet upon determining that the packet cannot be assigned the classification value based on the first number of at least one bit of the packet.

Abstract: A neural network system is described. The neural network system includes an artificial neural network including a plurality of neurons. One of the neurons includes an analog electrical circuit and the neurons are interconnected.