Fireeye, Inc.

Musarubra US LLC

Fireeye Security Holdings, Inc.

Emmanuel Thioux

Michael Vincent

Darien Kindlund

Sai Omkar Vashisht

Muhammad Amin

Ali Mesdaq

Abhishek Singh

Osman Abdoul Ismael

Alex Pilipenko

Sal Vashisht

Sai Vashisht

Yichong Lin

Alex Pilpenko

According to one embodiment, an non-transitory storage medium is configured to store a plurality of engines, which operate to conduct an analysis of a received object to determine if the object is associated with a malicious attack. The plurality of engines includes a first engine and a second engine. The first engine is configured to conduct a first analysis of the received object for anomalous behaviors including anomalous actions or omissions during virtual processing of the object that indicate the received object is malicious. The second engine is configured to conduct a second analysis corresponding to a classification of the object as being associated with a malicious attack. The analysis schemes conducted by the first engine and the second engine may be altered via configuration files, which adjusts (i) parameter value(s) or (ii) operation rules(s) to alter the analysis conducted by the first engine and/or second engine.

Dynamically remote tuning of a malware content detection system

According to one embodiment, an apparatus comprises a processor and memory. Communicatively coupled to the processor, the memory includes a detection module that, when executed, conducts an analysis of a received object to determine if the received object is associated with a malicious attack. The detection module is configurable, and thus, certain capabilities can be enabled, disabled or modified. The analysis is to be altered upon receipt of a configuration file that includes information to alter one or more rules controlling the analysis conducted by the detection module.

Techniques for detecting malicious content using simulated user interactions are described herein. In one embodiment, a monitoring module monitors activities of a malicious content suspect executed within a sandboxed operating environment. In response to detection of a predetermined event triggered by the malicious content suspect requesting a user action on a graphical user interface (GUI) presented by the malicious content suspect, simulating, a user interaction module simulates a user interaction with the GUI without user intervention. An analysis module analyzes activities of the malicious content suspect in response to the simulated user interaction to determine whether the malicious content suspect should be declared as malicious.

Malicious content analysis using simulated user interaction without user involvement

A method of detecting malware in a specimen of computer content or network traffic is described. The method features conducting a first analysis on the specimen in accordance with a first plurality of analyses and an order of the first plurality of analyses. A second analysis is conducted on the specimen different than the first analysis type. Thereafter, further analyses on the specimen may be altered by modifying information associated with the first plurality of analyses or the order of the first plurality of analyses in response to feedback information based on results from at least the first analysis. The modified information changes a malware analysis of the specimen from being conducted in accordance with the first plurality of analyses to being conducted in accordance with a second plurality of analyses different in analysis type or in order of analyses than the first plurality of analyses.

Dynamically adaptive framework and method for classifying malware using intelligent static, emulation, and dynamic analyses

According to one embodiment, a system of detecting malware in a specimen of computer content or network traffic comprises a processor and a memory. The memory includes a first analysis logic and a second analysis logic that may be executed by the processor. Upon execution, the first analysis logic performs a static analysis in accordance with an analysis plan to identify one or more suspicious indicators associated with malware and one or more characteristics related to processing of the specimen. The second analysis logic performs a second analysis in accordance with the analysis plan by processing of the specimen in a virtual machine and monitoring for one or more unexpected behaviors during virtual processing of the specimen in the virtual machine. The analysis plan may be altered based on the results of one of the analyzes.

System and method for classifying malware within content created during analysis of a specimen

A network device for detecting malware is described. The network device features a memory storage device and a controller. The controller operating in cooperation with one or more virtual machines that are based on software modules stored within the memory storage device. The controller is configured to (i) monitor behaviors of at least a first virtual machine of the one or more virtual machines processing data received over a network, (ii) identify at least one anomalous behavior that includes either a communication anomaly or an execution anomaly, and (iii) detect, based on the identified at least one anomalous behavior, a presence of malware in the first virtual machine in response to identifying the at least one anomalous behavior.

System and method for analysis of a memory dump associated with a potentially malicious content suspect

According to one embodiment, a threat detection system is integrated with at least a dynamic analysis engine. The dynamic analysis engine is configured to automatically determine whether one or more objects included in received network traffic contains a heap spray attack. Upon detection of a potential heap spray attack, the dynamic analysis engine may copy potential shellcode within an object included in the received network traffic, insert the copy of the potential shellcode into a second region of allocated memory and analyze the execution of the potential shellcode to determine whether characteristics associated with an exploit are present.

Shellcode detection

Techniques for malware detection are described. Herein, a system, which detects malware in a received specimen, comprises a processor and a memory. Communicatively coupled to the processor, the memory comprises a controller that controls analysis of the specimen for malware in accordance with an analysis plan. The memory further comprises (a) a static analysis module that performs at least a first static analysis to identify a suspicious indicator of malware and at least partially determine that the specimen includes a packed object; (b) an emulation analysis module that emulates operations associated with processing of the specimen by a software application or library, including unpacking an object of the specimen when the specimen is determined by the static analysis module to include the packed object, and monitors one or more behaviors of the specimen during the emulated operations; and a classifier that determines whether the specimen should be classified as malicious.

Malware analysis in accordance with an analysis plan

According to one embodiment, a threat detection system is integrated with at least a dynamic analysis engine. The dynamic analysis engine is configured to automatically detect a function call by an application, responsive to detecting the function call, analyze contents located at one or more addresses located within a portion of memory allocated for the application, and, based on the analysis, determine whether one or more objects included in received network traffic is associated with a return-oriented programming (ROP) exploit.

Return-oriented programming detection

According to one embodiment, a threat detection system is integrated with at least a dynamic analysis engine. The dynamic analysis engine is configured to automatically to detect potential shellcode at a first storage location within a region of memory allocated for an application, conduct a first search at one or more storage locations prior to the first storage location within the region of allocated memory for at least one or more patterns, conduct a second search at one or more storage locations subsequent to the first storage location within the region of allocated memory for at least one or more patterns, detect a first pattern at one or more storage locations prior to the first storage location within the region of allocated memory, and detect a second pattern at one or more storage locations subsequent to the first storage location with the region of allocated memory, wherein at least one of the first pattern or the second pattern is absent from a predefined list of patterns.

Computer exploit detection using heap spray pattern matching

Techniques for malicious content detection using memory dump are described herein. According to one embodiment, a monitoring module is configured to monitor activities of a malicious content suspect executed within a sandboxed operating environment. In response to detection of one or more predetermined events triggered by the malicious content suspect, a memory dump module is configured to generate a memory dump of the malicious content suspect. An analysis module is configured to analyze the memory dump to determine whether the malicious content suspect should be declared as malicious based on a set of one or more rules.

File extraction from memory dump for malicious content analysis

According to one embodiment, an apparatus comprises a processor and memory. Communicatively coupled to the processor, the memory comprises one or more detection modules each being software that is configurable to enable, disable or modify capabilities for that corresponding detection module. A first detection module the detection modules, when executed by the processor, conducts a first capability including an analysis of a received object to determine if the received object is associated with a malicious attack. The analysis may be altered upon receipt of a configuration file that is substantially lesser in size than the software forming the first detection module and includes information to alter one or more rules controlling the first capability.

Techniques for malware detection are described herein. According to one aspect, control logic determines an analysis plan for analyzing whether a specimen should be classified as malware, where the analysis plan identifies at least first and second analyses to be performed. Each of the first and second analyses identified in the analysis plan including one or both of a static analysis and a dynamic analysis. The first analysis is performed based on the analysis plan to identify suspicious indicators characteristics related to processing of the specimen. The second analysis is performed based on the analysis plan to identify unexpected behaviors having processing or communications anomalies. A classifier determines whether the specimen should be classified as malicious based on the static and dynamic analyses. The analysis plan, the indicators, the characteristics, and the anomalies are stored in a persistent memory.

Growing community of inventors

Santa Cruz, CA, United States of America

Emmanuel Thioux