公开(公告)号：US20240303511A1

公开(公告)日：2024-09-12

申请号：US18179137

申请日：2023-03-06

Applicant: Hewlett Packard Enterprise Development LP

Inventor： MADHUSOODHANA CHARI SESHA ,  Ramasamy Apathotharanan ,  Sumangala Bannur Subraya ,  Madhumitha Rajamohan ,  Azath Abdul Samadh ,  Chirag Dineshkumar Shah

IPC: G06N5/025 ,  G06F18/24 ,  H04L43/026

CPC classification number: G06N5/025 ,  G06F18/24765 ,  H04L43/026

Abstract: Systems and methods are provided for classifying network traffic flows across a network. Specifically, the network traffic flows are classified under a fully-segmented ruleset, wherein the fully segmented ruleset was generated by training a decision tree machine learning (“ML”) algorithm with a training dataset, and wherein each item of the training dataset satisfies the complete rule pathway to different leaf nodes of the fully segmented ruleset. Classification under a fully-segmented ruleset allowing for capture of idiosyncratic patterns specific to a given malicious source of network traffic flows. Further, systems and methods are provided allowing for a user to designate network traffic flows for classification of network traffic flows at different network devices, wherein the classification at different network devices may allow for more computationally intensive classification.

公开(公告)号：US20230136037A1

公开(公告)日：2023-05-04

申请号：US17515422

申请日：2021-10-30

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MADHUSOODHANA CHARI SESHA ,  YASHAS BELLUR YATHISH

IPC: H04L29/08 ,  H04L12/26 ,  H04L29/06

Abstract: Sessions are core components of communication between communicating systems, which may include, for example, a client device and a server. A network device can be used to monitor and analyze session information that is transmitted in a client-server communication. Visibility into the session information and the traffic flow of a network device is critical to improve the performance and security of the network device and the transmission of information in the client-server communication. A lack of visibility into the session information can reduce security, leading to viruses, malware, and malfunctions.

公开(公告)号：US20240104438A1

公开(公告)日：2024-03-28

申请号：US17954906

申请日：2022-09-28

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MADHUSOODHANA CHARI SESHA

IPC: G06N20/20 ,  G06K9/62

CPC classification number: G06N20/20 ,  G06K9/6256 ,  G06K9/6262

Abstract: Systems and methods for checking whether training data to be inputted into a training phase of a ML model is Independent and Identically Distributed data (IID data), and taking action based on that determination. One example of the present disclosure provides a method implemented by an edge node operating in a distributed swarm learning blockchain network. The method includes receiving a smart contract including a definition of conforming data and executing the smart contract including the definition of conforming data. The method further includes receiving one or more batches of training data for training a ML model. The method further includes checking whether each batch of training data conforms to the agreed-upon definition of conforming data, tagging and isolating non-conforming batches of training data, and inputting conforming batches of training data into a training phase of the machine learning model. The conforming batches of training data are IID data.

公开(公告)号：US20240168975A1

公开(公告)日：2024-05-23

申请号：US17991500

申请日：2022-11-21

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MANTEJ SINGH GILL ,  MADHUSOODHANA CHARI SESHA ,  DHAMODHRAN SATHYANARAYANAMURTHY ,  ANIL BABULAL

IPC: G06F16/28 ,  G06N20/00

CPC classification number: G06F16/285 ,  G06N20/00

Abstract: Systems and methods are provided for receiving a time series dataset from a monitored processor and group the dataset into a plurality of clusters. Using an unsupervised machine learning model, the system may combine a subset of the plurality of clusters by data signature similarities to form a plurality of motifs and combine the plurality of motifs into one or more shapelets. In some examples, the system may train a supervised machine learning model using the plurality of motifs and the one or more shapelets as input to the supervised machine learning model. The system can perform various actions in response to labelling the time series dataset, including predicting a second time series dataset, determining that a monitored processor corresponds with an overutilization at a particular time, or suggesting a reduction of additional utilization of the monitored processor.

公开(公告)号：US20230135485A1

公开(公告)日：2023-05-04

申请号：US17515309

申请日：2021-10-29

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MADHUSOODHANA CHARI SESHA ,  SUNIL SUKUMARAN

IPC: G08G1/01

Abstract: Systems and methods are provided for combining a multiple sub-time window sampling architecture with machine learning to detect outlier traffic flow behavior which may indicate malicious/problematic network activity. For example, a network device may obtain a sample of traffic flow data during a defined time window. The sample of traffic flow data may comprise information associated with a sampled subset of traffic flows transferred by a network device in the defined time window. The network device may partition the defined time window into two or more sub-time windows. In each sub-time window, using machine learning, the network device may assign an outlier-related classification to each sampled traffic flow based on the relative behavioral characteristics of all the sampled traffic flows. The network device may aggregate the outlier-related classifications for each sampled traffic flow across multiple sub-time windows, and process traffic flows based on the aggregated outlier-related classifications.

公开(公告)号：US20230308401A1

公开(公告)日：2023-09-28

申请号：US17701289

申请日：2022-03-22

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MADHUSOODHANA CHARI SESHA ,  KRISHNA MOHAN ELLURU ,  SHAUN WAKUMOTO

IPC: H04L49/55 ,  H04L49/00

CPC classification number: H04L49/555 ,  H04L49/557 ,  H04L49/3009

Abstract: Systems and methods are provided for collecting data related to changes to a data store table, which may be used for analyzing problems that occur in the network. The information monitored may include types of changes made to a data store/table, such as insertions and deletions of data store elements. When an anomaly occurs in the statistical data store/table data, an alert is issued. This statistical data of the types of changes to a data store may be suggestive of similar changes in a network. For example, the uptime, inactive time, and stable time of rows of a data store table may be used for estimating or inferring the uptime, inactive time, and stable time for nodes, data paths, or other elements of a network. The system may include a web UI or a command line interface, which may aid in diagnosing problems in the network, and taking corrective action.

公开(公告)号：US20230222395A1

公开(公告)日：2023-07-13

申请号：US17574409

申请日：2022-01-12

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MADHUSOODHANA CHARI SESHA

IPC: G06N20/20 ,  G06K9/62

CPC classification number: G06N20/20 ,  G06K9/6201 ,  G06K9/622

Abstract: Systems and methods are provided for implementing a distributed training by exchanging learnt parameters generated from unsupervised machine learning (ML) modeling. Each device in a distributed network may implement the unsupervised ML model to determine clusters of input data and/or determine a centroid of each determined cluster. The approximate centroid location of each cluster of data may be transmitted to other network devices in the local computing environment or other distributed computing environments. Each device may share their list of centroids of the clusters with other network devices (e.g., to implement swarm learning). These distributed network devices may compare the received centroids with centroids generated from a local ML model at each network device and initiate an action in response to the comparison.

公开(公告)号：US20230113462A1

公开(公告)日：2023-04-13

申请号：US17500896

申请日：2021-10-13

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： MADHUSOODHANA CHARI SESHA ,  ANKIT KUMAR SINHA

IPC: H04L12/751 ,  H04L12/741 ,  G06N20/00

Abstract: Systems and methods are provided for detecting changes in network activity that are depicted in a routing table. The routing table may be stored as a search tree data structure (e.g., Merkle Patricia Tree) to mimic a standard routing table and reduce the search time to find the desired route by allowing the router to traverse the search tree data structure more efficiently. Additionally, the metadata of the tree may be provided to an unstructured machine learning model (e.g., K-means) to identify new clusters of routes week-over-week and generate an alert with any changes. Changes are identified in near real time and dynamically at the router (not a central device) to reduce the time needed to respond to network changes.