Lastline, Inc.

Vmware, Inc.

Clemens Kolbitsch

Paolo Milani Comparetti

Ludovico Cavedon

Laxmikant Vithal Gunda

Rayanagouda Bheemanagouda Patil

Vaibhav Diwakar Kulkarni

Kedar Bhalchandra Chaudhari

Roman Vasilenko

The disclosure herein describes executing unknown processes while preventing sandbox-evading malware therein from performing malicious behavior. A process execution event associated with an executable is detected, wherein the executable is to be executed in a production environment. The executable is determined to be an unknown executable (e.g., an executable that has not been analyzed for malware) using signature data in the process execution event. A function call hook interface of a sandbox simulator is activated, and a process of the executable is executed in the production environment. Any function calls from the executing process are intercepted by the activated function call hook interface, and sandbox-style responses to the intercepted function call are generated using sandbox response data of the sandbox simulator. The generated sandbox responses are provided to the executing process, whereby malware included in the executable behaves as if the executing process is executing in a sandbox environment.

Preventing activation of malware by exhibiting sandbox behavior in a non-sandbox environment

A shadow sandbox is maintained for malware detection. The shadow sandbox is a virtual machine replica of a target computing environment from a protected computing system. The shadow sandbox is maintained through all change events that occur to the target computing environment. The described systems and methods of detecting or preventing malware execution include maintaining a virtual machine replica of a target computing system by monitoring the target computing system for a plurality of possible events, the plurality of possible events including change events and risk events, detecting a change event on the target computing system, and updating the virtual machine based on the detected change event. The described systems and methods detect a risk event on the target computing system, execute the risk event on the virtual machine, and determine whether the risk event is malicious based on observation of execution of the risk event on the virtual machine.

Methods and systems for maintaining a sandbox for use in malware detection

The present disclosure is directed to methods and systems for malware detection based on environment-dependent behavior. Generally, an analysis environment is used to determine how input collected from an execution environment is used by suspicious software. The methods and systems described identify use of environmental information to decide between execution paths leading to malicious behavior or benign activity. In one aspect, one embodiment of the invention relates to a method comprising monitoring execution of suspect computer instructions; recognizing access by the instructions of an item of environmental information; identifying a plurality of execution paths in the instructions dependant on a branch in the instructions based on a value of the accessed item of environmental information; and determining that a first execution path results in benign behavior and that a second execution path results in malicious behavior. The method comprises classifying the computer instructions as evasive malware responsive to the determination.

Methods and systems for malware detection based on environment-dependent behavior

The present disclosure is directed to methods and systems for reciprocal generation of watch-lists and traffic models characteristic of malicious network activity. In some aspects, the described methods and systems relate to maintaining data for recognition of malicious network activity. In general, the methods include monitoring network traffic; comparing endpoint data from monitored data packets to endpoints in a watch-list of network endpoints and comparing packet data from monitored data packets to traffic models in a catalog of traffic models characterizing malicious network activity; and determining, based on the comparisons, that a set of data packets comprise suspect network activity. The methods include adding a network endpoint to the watch-list when the determination is based on comparing packet data to a traffic model or adding a traffic model to the catalog when the determination is based on comparing endpoint data.

Growing community of inventors

Goleta, CA, United States of America

Clemens Kolbitsch