Juniper Networks, Inc.

Proofpoint, Inc.

Splunk Inc.

Cisco Technology, Inc.

Bryan Burns

Krishna Narayanaswamy

Steven Robert Sandke

Qingming Ma

Siying Yang

Daniel J Quinlan

Oskar Ibatullin

Gregory Lee Wittel

Julien Sobrier

Robert W Cameron

William Metcalf

Jason Williams

Steven Thomas Jackson

Li Fei Zheng

Oliver Kourosh Tavakoli

Michael P Horn

Vladimir Sukhanov

Kyle Adams

Harshad Bhaskar Nakil

David Robert Knight

Ankur Singla

Sheng Shuang Li

Venkata Rama Raju Manthena

Shan Yu

Xianzhi Li

Na Liu

Alexander S Waterman

Xuejun Wu

Alex Waterman

Ricardo Oliveira

Michael Horn

Yuly Tenorio Morales

Vipin Rawat

Michael Chuong Shieh

Christopher Anthony Iezzoni

Robert Cameron

Techniques are described for providing a threat analysis platform capable of automating actions performed to analyze security-related threats affecting IT environments. Users or applications can submit objects (e.g., URLs, files, etc.) for analysis by the threat analysis platform. Once submitted, the threat analysis platform routes the objects to dedicated engines that can perform static and dynamic analysis processes to determine a likelihood that an object is associated with malicious activity such as phishing attacks, malware, or other types of security threats. The automated actions performed by the threat analysis platform can include, for example, navigating to submitted URLs and recording activity related to accessing the corresponding resource, analyzing files and documents by extracting text and metadata, extracting and emulating execution of embedded macro source code, performing optical character recognition (OCR) and other types of image analysis, submitting objects to third-party security services for analysis, among many other possible actions.

Web analyzer engine for identifying security-related threats

Automated attack chain following by a threat analysis platform

File analysis engines for identifying security-related threats

The subject disclosure relates to methods for assessing cyber-security risks, and in particular for calculating a risk-index for multiple users of a computer network. In some implementations, a process of the disclosed technology includes steps for determining a privileged index for each of a plurality of network users, determining a vulnerability index for each of the plurality of network users, calculating a threat score for one or more cyber-security attacks directed at each of the plurality of network users, and calculating a risk-index for at least one network user from among the plurality of network users, wherein the risk-index is based on the privileged index, the vulnerability index, and the threat score associated with each of the network users. Systems and machine-readable media are also provided.

People-centric threat scoring

Provided herein are systems and methods for targeted attack protection using predictive sandboxing. In exemplary embodiments, a method includes retrieving a Uniform Resource Locator (URL) from a message of a user and performing a preliminary determination to see if the URL can be discarded if it is not a candidate for sandboxing. The exemplary method includes computing a plurality of selection criteria factors for the URL if the URL passes the preliminary determination, each selection criteria factor having a respective factor threshold. The method can further include determining if any of the selection criteria factors for the URL exceeds the respective factor threshold for the respective selection criteria factor. Based on the determining, if any of the selection criteria factors exceeds the factor threshold for the selection criteria factor, the exemplary method includes automatically placing the URL in a sandbox for analysis.

Targeted attack protection from malicious links in messages using predictive sandboxing

Systems and methods for targeted attack protection using predictive sandboxing

An intrusion detection system ('IDS') device is described that includes a flow analysis module to receive a first packet flow from a client and to receive a second packet flow from a server. The IDS includes a forwarding component to send the first packet flow to the server and the second packet flow to the client and a stateful inspection engine to apply one or more sets of patterns to the first packet flow to determine whether the first packet flow represents a network attack. The IDS also includes an application identification module to perform an initial identification of a type of software application and communication protocol associated with the first packet flow and to reevaluate the identification of the type of software application and protocol according to the second packet flow. The IDS may help eliminate false positive and false negative attack identifications.

Identifying applications for intrusion detection systems

Provided herein are systems and methods for targeted attack protection using predictive sandboxing. In exemplary embodiments, a method includes retrieving a URL from a message of a user and performing a preliminary determination to see if the URL can be discarded if it is not a candidate for sandboxing. The exemplary method includes computing a plurality of selection criteria factors for the URL if the URL passes the preliminary determination, each selection criteria factor having a respective factor threshold. The method can further include determining if any of the selection criteria factors for the URL exceeds the respective factor threshold for the respective selection criteria factor. Based on the determining, if any of the selection criteria factors exceeds the factor threshold for the selection criteria factor, the exemplary method includes automatically placing the URL in a sandbox for analysis.

A network device comprises one or more processors coupled to a memory, and a dynamic services module configured for execution by the one or more processors to receive, from a client device, a service request specifying a service. The dynamic service module is further configured for execution by the one or more processors to, in response to obtaining a negative indication for the service, send a representation of the service request to a honeypot to cause the honeypot to offer the service to the client device.

Dynamic service handling using a honeypot

Targeted attack protection using predictive sandboxing

Provided herein are systems and methods for targeted attack protection using predictive sandboxing. In exemplary embodiments, a method includes retrieving a URL from a message of a user and performing a preliminary determination to see if the URL can be discarded if it is not a candidate for sandboxing. The exemplary method includes computing a plurality of selection criteria factors for the URL if the URL passes the preliminary determination, each selection criteria factor having a respective factor threshold. The method can further include determining if any of the selection criteria factors for the URL exceeds the respective factor threshold for the respective selection criteria factor. Based on the determining, if any of the selection criteria factors exceeds the factor threshold for the selection criteria factor, the exemplary method automatically processes the URL using a sandbox.

This disclosure describes techniques for determining whether a network session originates from an automated software agent. In one example, a network device, such as a router, includes a network interface to receive packets of a network session, a bot detection module to calculate a plurality of scores for network session data based on a plurality of metrics, wherein each of the metrics corresponds to a characteristic of a network session originated by an automated software agent, to produce an aggregate score from an aggregate of the plurality of scores, and to determine that the network session is originated by an automated software agent when the aggregate score exceeds a threshold, and an attack detection module to perform a programmed response when the network session is determined to be originated by an automated software agent. Each score represents a likelihood that the network session is originated by an automated software agent.

Detecting malicious network software agents

Growing community of inventors

Portland, OR, United States of America

Bryan Burns