Fireeye, Inc.

Symantec Corporation

Shivani Deshpande

Yasir Khalid

Muhammad Amin

Shrikrishna Karandikar

Abhishek Singh

Amit Malik

Wei Zheng

Andrew John Mastracci

Suresh Babu Muppala

A malware detection system configured to detect suspiciousness in obfuscated content. A multi-stage static detection logic is utilized to detect obfuscation, make the obfuscated content accessible, identify suspiciousness in the accessible content and filter non-suspicious non-obfuscated content from further analysis. The multi-stage static detection logic includes a controller, a de-constructor, and a post-processor. The controller is configured to receive content while the de-constructor configured to receive content from the controller and deconstruct the content using the analysis technique selected by the controller. The post-processor is configured to receive the de-constructed content from the de-constructor, determine whether a specimen within the de-constructed content is suspicious, and remove non-suspicious content from further analysis.

Multistage system and method for analyzing obfuscated content for malware

In an embodiment, a system, device and method for detecting a malicious attack is described. Herein, the system includes a security network device that conducts an analysis on received network traffic to detect a suspicious object associated with the network traffic and determine an identifier associated with a source of the suspicious object. Information associated with the suspicious object and/or ancillary data, including information that identifies a return path for analysis results to a customer, are uploaded to a detection cloud. The detection cloud includes provisioning logic and one or more virtual machines that are provisioned by the provisioning logic in accordance with at least a portion of the ancillary data. The provisioning logic to customize functionality of the detection cloud for a specific customer.

System, device and method for detecting a malicious attack based on direct communications between remotely hosted virtual machines and malicious web servers

A computerized technique wherein a received object is analyzed using a plurality of information sources to determine context information, wherein one information source comprises configuration information determined from a client device. One or more software profiles are generated based on the context information in order to provision one or more virtual machines of a dynamic analysis logic system. One or more work orders are generated based on the one or more software profiles. A priority order is assigned to the one or more software profiles. A dynamic analysis is scheduled based on the work orders and the assigned priority order to determine one or more susceptible software environments, and an alert is generated comprising information to update one or more susceptible environments in real time.

Susceptible environment detection system

A malware detection system configured to detect suspiciousness in obfuscated content. A multi-stage static detection logic is utilized to detect obfuscation, make the obfuscated content accessible, identify suspiciousness in the accessible content and filter non-suspicious non-obfuscated content from further analysis. The system is configured to identify obfuscated content, de-obfuscate obfuscated content, identify suspicious characteristics in the de-obfuscated content, execute a virtual machine to process the suspicious network content and detect malicious network content while removing from further analysis non-suspicious network content.

In an embodiment, a system, device and method for detecting a malicious attack is described. Herein, the system includes a security network device that conducts an analysis on received network traffic to detect a suspicious object associated with the network traffic and determine an identifier associated with a source of the suspicious object. Both information associated with the suspicious object and ancillary data, including information that identifies a return path for analysis results to a customer, are uploaded to a detection cloud. The detection cloud includes provisioning logic and one or more virtual machines that are provisioned by the provisioning logic in accordance with at least a portion of the ancillary data. The provisioning logic to customize functionality of the detection cloud for a specific customer.

Network-based malware detection

Herein described is a collection of traffic classifiers communicatively coupled to a classification aggregator. Traffic classifiers may use conventional techniques to classify network traffic by application name, and thereafter may construct mappings that are used to more efficiently classify future network traffic. Mappings may associate one or more characteristics of a communication flow with an application name. In a collaborative approach, these mappings are shared among the traffic classifiers by means of the classification aggregator so that one traffic classifier can leverage the intelligence (e.g., mappings) formulated by another traffic classifier.

Collaborative application classification

In an embodiment, a threat detection and prevention system comprises a network-traffic static analysis logic and a classification engine. The network-traffic static analysis logic is configured to conduct an analysis of a multi-flow object by analyzing characteristics of the multi-flow object and determining if the characteristics of the multi-flow object is associated with a malicious attack such as being indicative of an exploit for example. The classification engine is configured to receive results of the analysis of the multi-flow object and, based on the results of the analysis of the multi-flow object, determine whether the multi-flow object is associated with a malicious attack.

System, apparatus and method for detecting a malicious attack based on static analysis of a multi-flow object

In an embodiment, a dynamic analysis engine is configured to receive an identifier associated with a source for network traffic including at least one object having at least a prescribed probability of being associated with an exploit. Deployed within a detection cloud, the dynamic analysis engine comprises one or more virtual machines and monitoring logic. The virtual machines are adapted to virtually process the identifier by establishing a communication session with a server hosting a website accessible by the identifier. In communication with the virtual machines, the monitoring logic is adapted to detect anomalous behaviors by the virtual machines during the communication session with the server.

Growing community of inventors

Fremont, CA, United States of America

Shivani Deshpande