Sable Networks, Inc.

Surya K Pappu

Sharad R Murthy

Paul Jezioranski

Sanjay Oza

Various embodiments of the present invention relating to a subscriber fairness solution are disclosed. The subscriber fairness solution contemplates a variety of improved techniques for using a flow-based statistical collection mechanism to monitor subscriber usage across various attributes (e.g., subscriber byte count, flow count, etc.) and maintain subscriber usage information for different time frames. In embodiments, the subscriber fairness solution includes a detection phase and a mitigation phase. In the detection phase, 'outliers,' or subscribers who are using more than their fair share of network resources, are identified. In the mitigation phase, appropriate action is taken to resolve the constraints on the network resources, caused by these outliers. The subscriber fairness solution may be embodied in hardware, software, or a composite approach of both hardware and software.

System and method for ensuring subscriber fairness using outlier detection

Methods and systems for detecting and mitigating high-rate Distributed Denial of Service (DDoS) attacks are herein described. The present invention contemplates a variety of improved techniques for using a flow-based statistical collection mechanism to monitor and detect deviations in server usage data. The method further includes combining multiple anomaly algorithms in a unique way to improve the accuracy of identifying a high-rate DDoS attack. The DDoS solution includes a two-phase approach of detection and mitigation, both of which operate on a local- and a global-basis. Moreover, the anomaly algorithms can be modified or extrapolated to obtain the traffic deviation parameters and therefore, the attack probabilities.

Methods and systems for detecting and mitigating a high-rate distributed denial of service (DDoS) attack

A mechanism is disclosed for identifying, classifying, and controlling flows in a network. In one implementation, a separate set of behavioral statistics is maintained for each flow. These behavioral statistics are updated as packets belonging to a flow are processed. Whenever a packet belonging to a flow is processed, a set of policies that the flow's behavioral statistics satisfy is determined. For each policy that the flow's behavioral statistics satisfy, actions that are associated with that policy are applied relative to the packet. The actions may be designed to cause a router to handle, in a user-specified manner, packets that are likely to represent a particular kind of traffic. Thus, different flows, such as VOIP, gaming, streaming, and P2P flows, which are associated with different behavioral statistics, may be handled in ways applicable for the specific application traffic type.

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Milpitas, CA, United States of America

Surya K Pappu