Abstract: Described herein relates to a system and method for the prevention of a malicious attack on a computing resource. In embodiments, the system may comprise the following, including but not limited to: (1) at least one processor; and (2) computer memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations including: (a) observing traffic flow of a network; (b) altering a SYN threshold value based on the observing of the traffic flow of the network; (c) comparing a metric of SYN messages submitted to the network; and (d) based on the comparison of the metric of SYN messages submitted, selectively engaging corrective action with the network.

Abstract: In one implementation, a system for the prevention of malicious attack on a computing resource includes one or more processor; computer memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: observing traffic flow of a network; altering a SYN threshold value based on the observing of the traffic flow of the network; comparing a metric of SYN messages submitted to the network; and based on the comparison of the metric of SYN messages submitted, selectively engaging corrective action with the network.

Abstract: The centralized control capability of Software Defined Networking (SDN) presents a unique opportunity for enabling Quality of Service (QoS) routing. For delay sensitive traffic flows, a QoS mechanism efficiently computes path latency and minimizes a controller's response time. At the core of the challenges is how to handle short term network state fluctuations in terms of congestion and latency while guaranteeing the end-to-end latency performance of networking services. The disclosed technology provides a systematic framework that considers active link latency measurements, efficient statistic estimate of network states, and fast adaptive path computation. The disclosed technology can be implemented, for example, as an SDN controller application, and can find optimal end-to-end paths with minimum latency and significantly reduce the control overhead.

Abstract: In one implementation, a system for the prevention of malicious attack on a computing resource includes one or more processor; computer memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including: observing traffic flow of a network; altering a SYN threshold value based on the observing of the traffic flow of the network; comparing a metric of SYN messages submitted to the network; and based on the comparison of the metric of SYN messages submitted, selectively engaging corrective action with the network.

Abstract: The centralized control capability of Software Defined Networking (SDN) presents a unique opportunity for enabling Quality of Service (QoS) routing. For delay sensitive traffic flows, a QoS mechanism efficiently computes path latency and minimizes a controller's response time. At the core of the challenges is how to handle short term network state fluctuations in terms of congestion and latency while guaranteeing the end-to-end latency performance of networking services. The disclosed technology provides a systematic framework that considers active link latency measurements, efficient statistic estimate of network states, and fast adaptive path computation. The disclosed technology can be implemented, for example, as an SDN controller application, and can find optimal end-to-end paths with minimum latency and significantly reduce the control overhead.

Abstract: Data communication in network traffic is modeled in real time and is analyzed using a 2-state Markov modified Poissen process (MMPP). The traffic inter-arrival times for bursty and idle states define a transition window [?1max, ?2min] represented by the boundary values ?1max for the inter-arrival time for bursty traffic, and ?2min for the inter-arrival time for idle traffic. Changes in the values of ?1max and ?2min are tracked over time, and the size of the transition window is enlarged or decreased based upon relative changes in these values. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. The modeling is applicable to the synchronization of polling and blocking in a low-latency network system. This permits the adoptive selection of poll or block to maximize CPU utilization and interrupt latency.

Abstract: Data communication in network traffic is modeled in real time and is analyzed using a 2-state Markov modified Poissen process (MMPP). The traffic inter-arrival times for bursty and idle states define a transition window [?1max, ?2min] represented by the boundary values ?1max for the inter-arrival time for bursty traffic, and ?2min for the inter-arrival time for idle traffic. Changes in the values of ?1max and ?2min are tracked over time, and the size of the transition window is enlarged or decreased based upon relative changes in these values. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. The modeling is applicable to the synchronization of polling and blocking in a low-latency network system. This permits the adoptive selection of poll or block to maximize CPU utilization and interrupt latency.

Abstract: Data communication in network traffic is modeled in real time and is analyzed using a 2-state Markov modified Poissen process (MMPP). The traffic inter-arrival times for bursty and idle states define a transition window [?1max, ?2min] represented by the boundary values ?1max for the inter-arrival time for bursty traffic, and ?2min for the inter-arrival time for idle traffic. Changes in the values of ?1max and ?2min are tracked over time, and the size of the transition window is enlarged or decreased based upon relative changes in these values. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. The modeling is applicable to the synchronization of polling and blocking in a low-latency network system. This permits the adoptive selection of poll or block to maximize CPU utilization and interrupt latency.

Abstract: Self-similar data communication in network traffic is modeled real time and is analyzed using a Markov modified Poissen process (MMPP) to characterize the traffic flow and to accommodate high variability in traffic flow from one time period to the other. The analysis is performed at multiple time levels using a bottom-up approach. The parameters of the model are adjustable at each level according to the traffic parameters at that level. Each model consists of 2 states of network traffic behavior comprising a bursty state representing heavy traffic conditions and an idle state representing light traffic conditions. A transition window defines the upper time interval for the receipt of packets in the bursty state and the lower time interval for the receipt of packets in the idle state. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model.

Abstract: Data communication in network traffic is modeled in real time and is analyzed using a 2-state Markov modified Poissen process (MMPP). The traffic inter-arrival times for bursty and idle states define a transition window [?1max, ?2min] represented by the boundary values ?1max max for the inter-arrival time for bursty traffic, and ?2min for the inter-arrival time for idle traffic. Changes in the values of ?1max and ?2min are tracked over time, and the size of the transition window is enlarged or decreased based upon relative changes in these values. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. The modeling is applicable to the synchronization of polling and blocking in a low-latency network system. This permits the adoptive selection of poll or block to maximize CPU utilization and interrupt latency.

Abstract: Data communication in network traffic is modeled in real time and is analyzed using a 2-state Markov modified Poissen process (MMPP). The traffic inter-arrival times for bursty and idle states define a transition window [&lgr;1max, &lgr;2min] represented by the boundary values &lgr;1max for the inter-arrival time for bursty traffic, and &lgr;2min for the inter-arrival time for idle traffic. Changes in the values of &lgr;1max and &lgr;2min are tracked over time, and the size of the transition window is enlarged or decreased based upon relative changes in these values. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model. The modeling is applicable to the synchronization of polling and blocking in a low-latency network system. This permits the adoptive selection of poll or block to maximize CPU utilization and interrupt latency.

Abstract: Self-similar data communication in network traffic is modeled real time and is analyzed using a Markov modified Poissen process (MMPP) to characterize the traffic flow and to accommodate high variability in traffic flow from one time period to the other. The analysis is performed at multiple time levels using a bottom-up approach. The parameters of the model are adjustable at each level according to the traffic parameters at that level. Each model consists of 2 states of network traffic behavior comprising a bursty state representing heavy traffic conditions and an idle state representing light traffic conditions. A transition window defines the upper time interval for the receipt of packets in the bursty state and the lower time interval for the receipt of packets in the idle state. If the inter-rival times for the bursty state and the idle state become approximately equal, the model defaults to a single state model.