FireEye, Inc. is a publicly listed US network security company that aims to provide automated threat forensics and dynamic malware protection against advanced cyber threats, such as advanced persistent threats and spear phishing. Founded in 2004, the company is headquartered in Milpitas, California. The company's main product line consists of the Malware Protection System for web security, email security, file security, and malware analysis.Major investors include Sequoia Capital, Norwest Venture Partners, JAFCO Ventures, SVB Capital, DAG Ventures, Juniper Networks, and In-Q-Tel.On December 30, 2013, FireEye acquired Mandiant in a stock and cash deal worth in excess of $1 billion.On May 8, 2014, FireEye acquired company nPulse Technologies for approximately $60 million. nPulse will build on FireEye's ability to detect intrusions in a companies network by making it easier to track the intrusion and better understand its effect on the network. Wikipedia.
FireEye | Date: 2015-12-11
According to one embodiment, a malware classification scheme operating with an electronic device, configured with one or more hardware processors and a memory that stores the software handling the malware classification scheme that is conducted through analysis of behavior-based rules, is described. This malware classification scheme (i) conducts a determination whether a sequence of rules correspond to potential malicious behaviors detected during analysis of a malware sample within one or more virtual machines, and in response to determining that the sequence of rules corresponds to potential malicious behaviors, (ii) conducts an attempt to classify the malware sample to at least one known malware family based on an analysis of the sequence of rules.
FireEye | Date: 2016-03-25
Selective virtualization of resources is provided, where the resources may be intercepted and services or the resources may be intercepted and redirected. Virtualization logic monitors for a first plurality of requests that are initiated during processing of an object within the virtual machine. Each of the first plurality of requests, such as system calls for example, is associated with an activity to be performed in connection with one or more resources. The virtualization logic selectively virtualizes resources associated with a second plurality of requests that are initiated during the processing of the object within the virtual machine, where the second plurality of requests is lesser in number than the first plurality of requests.
FireEye | Date: 2015-06-30
According to one embodiment, a threat detection platform features a housing, a communication interface, a processor coupled to the communication interface, and a data store. The data store includes (i) an event log, (ii) a first virtual machine, and (iii) a second virtual machine. The first virtual machine is provisioned with a first guest image that is based on an instrumented software profile that includes a first software component and activity monitors configured for the first software component. The second virtual machine is provisioned with a second guest image that is based on a temporary software profile that includes a second software component that is a more recent version of the first software component and the activity monitors configured for the first software component.
FireEye | Date: 2015-11-02
A threat-aware microvisor may be deployed in a malware detection endpoint architecture and execute on an endpoint to provide exploit and malware detection within a network environment. Exploit and malware detection on the endpoint may be performed in accordance with one or more processes embodied as software modules or engines configured to detect suspicious and/or malicious behaviors of an operating system process (object), and to correlate and classify the detected behaviors as indicative of malware. Detection of suspicious and/or malicious behaviors may be performed by static and dynamic analysis of the object. Static analysis may perform examination of the object to determine whether it is suspicious, while dynamic analysis may instrument the behavior of the object as the operating system process runs via capability violations of, e.g. operating system events. A behavioral analysis logic engine and a classifier may thereafter cooperate to perform correlation and classification of the detected behaviors.
FireEye | Date: 2015-02-06
A trusted threat-aware microvisor may be deployed as a module of a trusted computing base (TCB). The microvisor is illustratively configured to enforce a security policy of the TCB, which may be implemented as a security property of the microvisor. The microvisor may manifest (i.e., demonstrate) the security property in a manner that enforces the security policy. Trustedness denotes a predetermined level of confidence that the security property is demonstrated by the microvisor. The predetermined level of confidence is based on an assurance (i.e., grounds) that the microvisor demonstrates the security property. Trustedness of the microvisor may be verified by subjecting the TCB to enhanced verification analysis configured to ensure that the TCB conforms to an operational model with an appropriate level of confidence over an appropriate range of activity. The operational model may then be configured to analyze conformance of the microvisor to the security property. A combination of conformance by the microvisor to the operational model and to the security property provides assurance (i.e., grounds) for the level of confidence and, thus, verifies trustedness.
FireEye | Date: 2015-06-22
An electronic message is analyzed for malware contained in the message. Text of an electronic message may be analyzed to detect and process malware content in the electronic message itself. The present technology may analyze an electronic message and attachments to electronic messages to detect a uniform resource location (URL), identify whether the URL is suspicious, and analyze all suspicious URLs to determine if they are malware. The analysis may include re-playing the suspicious URL in a virtual environment which simulates the intended computing device to receive the electronic message. If the re-played URL is determined to be malicious, the malicious URL is added to a black list which is updated throughout the computer system.
FireEye | Date: 2016-03-21
A computerized method for classifying objects in a malware system is described. The method includes detecting behaviors of an object for classification after processing of the object has begun. Data associated with the detected behaviors is collected, and a fuzzy hash for the received object is generated. The generation of the fuzzy hash may include (i) removing a portion of the data associated with the detected behaviors, and (ii) performing a hash operation on a remaining portion of the data associated with the detected behaviors. Thereafter, the fuzzy hash for the received object is compared to a fuzzy hash of an object in a preexisting cluster to generate a similarity measure. The received object is associated with the preexisting cluster in response to determining that the similarity measure is above a predefined threshold value. Thereafter, the results are reported.
FireEye | Date: 2015-01-21
A trusted threat-aware microvisor may be deployed as a module of a trusted computing base (TCB) that also includes a root task module configured to cooperate with the microvisor to load and initialize one or more other modules executing on a node of a network environment. The root task may cooperate with the microvisor to allocate one or more kernel resources of the node to those other modules. As a trusted module of the TCB, the microvisor may be configured to enforce a security policy of the TCB that, e.g., prevents alteration of a state related to security of the microvisor by a module of or external to the TCB. The security policy of the TCB may be implemented by a plurality of security properties of the microvisor. Trusted (or trustedness) may therefore denote a predetermined level of confidence that the security property is demonstrated by the microvisor.
FireEye | Date: 2016-04-04
A computer worm defense system comprises multiple containment systems tied together by a management system. Each containment system is deployed on a separate communication network and contains a worm sensor and a blocking system. In various embodiments, the computer worm may be transported from a production network, where the computer worm is not readily identifiable, to an alternate network in the worm sensor where the computer worm may be readily identifiable. Computer worm identifiers generated by a worm sensor of one containment system can be provided not only to the blocking system of the same containment system, but can also be distributed by the management system to blocking systems of other containment systems.
FireEye | Date: 2015-06-15
According to one embodiment, a virtualized malware detection system is integrated with a virtual machine host including a plurality of virtual machines and a security virtual machine. Logic within the virtual machines are configured to perform a dynamic analysis of an object and monitor for the occurrence of a triggering event. Upon detection of a triggering event within a virtual machine, the logic within the virtual machine provides the security virtual machine with information associated with the triggering event for further analysis. Based on the further analysis, the object may then be classified as non-malicious, or malicious.