Zhiqiang G Gao

8091132, Jan 3, 2012

Mar 5, 2007

11/682119

Nirwan Ansari - Montville NJ, US
Zhiqiang Gao - Westfield NJ, US

New Jersey Institute of Technology - Newark NJ

G06F 12/14

726 23

Embodiments are directed toward a method for Behavior-based Traffic Differentiation (BTD) that initially receives incoming packets and performs traffic classification to determine the protocol of the incoming packets. In addition, BTD performs bandwidth division/allocation to further support traffic classification amongst non-TCP traffic such as UDP and ICMP. For TCP traffic, the method for BTD determines whether a TCP connection has been established and performs at least one of rate limiting, waiting time reduction for half-open connections, and incrementing backlog queue size when the TCP connection has not been established. If the TCP connection has been established successfully, the method for BTD further includes proactive tests for traffic differentiation which identify normal traffic, which is admitted, and attack traffic, which is dropped.

20070206605, Sep 6, 2007

Mar 1, 2007

11/680837

Nirwan Ansari - Montville NJ, US
Zhiqiang Gao - Westfield NJ, US

New Jersey Institute of Technology - Newark NJ

H04L 12/28

3703955

Embodiments are directed to an Autonomous System-based Edge Marking (ASEM) for Internet Protocol (IP) traceback. In particular, the embodiments are a system and a method for IP traceback that receives one or more packets at routers; inscribes packets only at marking routers with autonomous system (AS) level and marking information; and forwards the marked packets to edge routers and other routers for verification. Additionally the packets are marked based on a probability measure and Border Gateway Protocol (BGP) routing table information is the AS level information used for marking and verification.

20200027105, Jan 23, 2020

Jul 20, 2018

16/040673

- New York NY, US
Jiafei Sun - Edison NJ, US
Zhiqiang Gao - Westfield NJ, US
Shahin Mahmoud Shahin - Clifton NJ, US
Erwan Le Doeuff - Hoboken NJ, US

G06Q 30/02
G06N 3/04
G06N 3/08
G06Q 40/06

Systems and methods for Value at Risk anomaly detection using a hybrid of deep learning and time series models are disclosed. In one embodiment, in an information processing device comprising at least one computer processor, a method for Value at Risk (VaR) anomaly detection, may include: (1) calculating a current period VaR; (2) training at least one hybrid model using historical VaR calculations; (3) calculating a forecasted VaR value using the at least one hybrid model; (4) detecting an anomaly based on the current period VaR and the forecasted VaR; and (5) generating a notification in response to the detected anomaly.