Cisco Technology, Inc.

Ellen Christine Scheib

Navindra Yadav

Omid Madani

Ali Parandehgheibi

Vimalkumar Jeyakumar

Mohammadreza Alizadeh Attar

Abhishek Ranjan Singh

Ashutosh Kulshreshtha

Khawar Deen

Jackson Ngoc Ki Pang

Supreeth Hosur Nagesh Rao

Shashidhar R Gandham

Anubhav Gupta

Shih-Chun Chang

Hai Trong Vu

Rohit Chandra Prasad

Hoang Viet Nguyen

Varun Sagar Malhotra

Sunil Gupta

Michael Standish Watts

Roberto Fernando Spadaro

Bharathwaj Sankara Viswanathan

Fnu Rachita Agasthy

Duane Thomas Barlow

Rachita Agasthy

Sunil Kumar Gupta

An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

System for monitoring and managing datacenters

This disclosure generally relate to a method and system for mapping application dependency information. The present technology relates techniques that enable user-adjustable application dependency mapping of a network system. By collecting internal network data using various sensors in conjunction with external user inputs, the present technology can provide optimized application dependency mapping using user inputs.

System and method for user optimized application dependency mapping

Systems and methods are provided for automatically discovering applications/clusters in a network and mapping dependencies between the applications/clusters. A network monitoring system can capture network flow data using sensors executing on physical and/or virtual servers of the network and sensors executing on networking devices connected to the servers. The system can determine a graph including nodes, representing at least the servers, and edges, between pairs of the nodes of the graph indicating the network flow data includes one or more observed flows between pairs of the servers represented by the pairs of the nodes. The system can determine a dependency map, including representations of clusters of the servers and representations of dependencies between the clusters, based on the graph. The system can display a first representation of a first cluster of the dependency map and information indicating a confidence level of identifying the first cluster.

MDL-based clustering for dependency mapping

This disclosure generally relates to a method and system for generating a communication graph of a network using an application dependency mapping (ADM) pipeline. In one aspect of the disclosure, the method comprises receiving network data (e.g., flow data and process information at each node) from a plurality of sensors associated with a plurality of nodes of the network, determining a plurality of vectors and an initial graph of the plurality of nodes based upon the network data, determining similarities between the plurality of vectors, clustering the plurality of vectors into a plurality of clustered vectors based upon the similarities between the plurality of vectors, and generating a communication graph of the network system based upon the plurality of clustered vectors.

Generate a communication graph using an application dependency mapping (ADM) pipeline

Systems, methods, and computer-readable media are provided for determining whether a node in a network is a server or a client. In some examples, a system can collect, from one or more sensors that monitor at least part of data traffic being transmitted via a pair of nodes in a network, information of the data traffic. The system can analyze attributes of the data traffic such as timing, port magnitude, degree of communication, historical data, etc. Based on analysis results and a predetermined rule associated with the attributes, the system can determine which node of the pair of nodes is a client and which node is a server.

Server-client determination

Application dependency mapping (ADM) can be automated in a network. The network can determine whether certain nodes form a cluster of a tier of an application. The network can monitor network data and process data for traffic passing through the network using a sensor network that provides multiple perspectives for the traffic. The network can analyze the network data and process data to determine respective feature vectors for nodes. A feature vector may represent a combination of the features corresponding to the network data and the features corresponding to the process data of a node. The network can compare the similarity of the respective feature vectors and determine each node's cluster based on similarity measures between nodes.

Cluster discovery via multi-domain fusion for application dependency mapping

An example method can include monitoring a network to identify flows between nodes in the network. Once flows have been identified, the flows can be tagged and labelled according to the type of traffic they represent. If a flow represents malicious or otherwise undesirable traffic, it can be tagged accordingly. A request can then be made for a reputation score of an entity which can identify one or more nodes of the network.

Determining a reputation of a network entity

This disclosure generally relate to a method and system for mapping network information. The present technology relates techniques that enable full-scale, dynamic network mapping of a network system. By collecting network and computing data using built-in sensors, the present technology can provide network information for system monitoring and maintenance. According to some embodiments, the present technology enables generating and displaying of network connections and data processing statistics related to numerous nodes in a network. The present technology provides useful insights and actionable knowledge for network monitoring, security, and maintenance, via intelligently summarizing and effectively displaying the complex network communications and processes of a network.

System and method for network information mapping and displaying

Creating classifiers for servers and clients in a network

In one embodiment, a method includes receiving network data at an analytics device, grouping features of the network data into multivariate bins, generating a density for each of the multivariate bins, computing a rareness metric for each of the multivariate bins based on a probability of obtaining a feature in a bin and the probability for all other of the multivariate bins with equal or smaller density, and identifying anomalies based on computed rareness metrics. An apparatus and logic are also disclosed herein.

Method and apparatus for computing cell density based rareness for use in anomaly detection

Growing community of inventors

Mountain View, CA, United States of America

Ellen Christine Scheib