Nipun Arora

20130318505, Nov 28, 2013

May 24, 2013

13/901964

Hui Zhang - Princeton Junction NJ, US
Nipun Arora - Plainsboro NJ, US
Guofei Jiang - Princeton NJ, US
Kenji Yoshihira - Princeton Junction NJ, US

G06F 11/36

717128

The invention efficiently provides user code information for kernel level tracing approaches. It applies an advanced variation of stack walking called multi-mode stack walking to the entire system level and generates the unified trace where the user code and kernel events are integrated. The invention uses runtime stack information and internal kernel data structures. Therefore, source code for user level code and libraries are not required for inspection. The invention introduces the mechanism to narrow down the monitoring focus to specific application software and improve monitoring performance.

20140068351, Mar 6, 2014

Aug 28, 2013

14/012000

Hui Zhang - Princeton Junction NJ, US
Nipun Arora - Plainsboro NJ, US
Guofei Jiang - Princeton NJ, US
Kenji Yoshihira - Princeton Junction NJ, US

NEC LABORATORIES AMERICA, INC. - Princeton NJ

G06F 11/30

714 473

A computer implemented method provides efficient monitoring and analysis of a program's memory objects in the operation stage. The invention can visualize and analyze a monitored program's data status with improved semantic information without requiring source code at runtime. The invention can provide higher quality of system management, performance debugging, and root-cause error analysis of enterprise software in the production stage.

20130290936, Oct 31, 2013

Apr 30, 2013

13/873610

Hui Zhang - Princeton Junction NJ, US
Nipun Arora - Plainsboro NJ, US
Guofei Jiang - Princeton NJ, US
Kenji Yoshihira - Princeton Junction NJ, US

NEC Laboratories America, Inc. - Princeton NJ

G06F 11/36

717128

A system for automatically instrumenting and tracing an application program and related software components achieves a correlated tracing of the program execution. It includes tracing of endpoints that are the set of functions in the program execution path that the developers are interested. The tracing endpoints and related events become the total set of functions to be traced in the program (called instrument points). This invention automatically analyzes the program and generates such instrumentation points to enable correlated tracing. The generated set of instrumentation points addresses common questions that developers ask when they use monitoring tools.

20190098048, Mar 28, 2019

Aug 13, 2018

16/101794

- Princeton NJ, US
Nipun Arora - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
Bo Zong - New York NY, US
Daeki Cho - Highland Park NJ, US
Mingda Li - Los Angeles CA, US

H04L 29/06
H04L 12/733
H04L 12/741
H04L 12/26
G06N 3/08
G06K 9/62

Methods and systems for mitigating a spoofing-based attack include calculating a travel distance between a source Internet Protocol (IP) address and a target IP address from a received packet based on time-to-live information from the received packet. An expected travel distance between the source IP address and the target IP address is estimated based on a sparse set of known source/target distances. It is determined that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security action is performed responsive to the determination that the received packet has a spoofed source IP address.

20190098049, Mar 28, 2019

Aug 13, 2018

16/101815

- Princeton NJ, US
Nipun Arora - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
Bo Zong - New York NY, US
Daeki Cho - Highland Park NJ, US
Mingda Li - Los Angeles CA, US

H04L 29/06
H04L 12/733
H04L 12/751
H04L 12/893

Endpoint security systems and methods include a distance estimation module configured to calculate a travel distance between a source Internet Protocol (IP) address and an IP address for a target network endpoint system from a received packet received by the target network endpoint system based on time-to-live (TTL) information from the received packet. A machine learning model is configured to estimate an expected travel distance between the source IP address and the target network endpoint system IP address based on a sparse set of known source/target distances. A spoof detection module is configured to determine that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security module is configured to perform a security action at the target network endpoint system responsive to the determination that the received packet has a spoofed source IP address.

20190098050, Mar 28, 2019

Aug 13, 2018

16/101834

- Princeton NJ, US
Nipun Arora - Plainsboro NJ, US
Haifeng Chen - West Windsor NJ, US
Bo Zong - New York NY, US
Daeki Cho - Highland Park NJ, US
Mingda Li - Los Angeles CA, US

H04L 29/06
H04L 12/733
H04L 12/741
H04L 12/26
G06F 15/18

Endpoint security systems and methods include a distance estimation module configured to calculate a travel distance between a source Internet Protocol (IP) address and an IP address for a target network endpoint system from a received packet received by a network gateway system based on time-to-live (TTL) information from the received packet. A machine learning model is configured to estimate an expected travel distance between the source IP address and the target network endpoint system IP address based on a sparse set of known source/target distances. A spoof detection module is configured to determine that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security module is configured to perform a security action at the network gateway system responsive to the determination that the received packet has a spoofed source IP address.

20180365291, Dec 20, 2018

May 18, 2018

15/983404

- Princeton NJ, US
Youfu Li - Los Angeles CA, US
Daeki Cho - Highland Park NJ, US
Bo Zong - New York NY, US
Nipun Arora - Plainsboro NJ, US
Cristian Lumezanu - Princeton Junction NJ, US

G06F 17/30

Systems and methods for optimizing query execution to improve query processing by a computer are provided. A query is analyzed and translated into a logical plan. A runtime query optimizer is applied to the logical plan to identify a physical plan including operators for execution. The logical plan is translated into the physical plan. Execution of the query is scheduled according to the physical plan.

20180365294, Dec 20, 2018

May 18, 2018

15/983356

- Princeton NJ, US
Nipun Arora - Plainsboro NJ, US
Hui Zhang - Princeton Junction NJ, US

G06F 17/30
G06N 5/04
G06N 99/00

Systems and methods for implementing a behavior analysis engine (BAE) to improve computer query processing are provided. A job request to execute an input rule on target log data is received by a BAE service via a user interface. The job request is executed by the BAE service to generate a result by obtaining the input rule from a rule-base, parsing the input rule to create a data structure, optimizing the data structure, and executing one or more operations using the optimized data structure. The result is stored by the BAE service in a result database.