Cisco Technology, Inc.

Alfred Huger

Oliver Friedrichs

Adam J O'Donnell

Zulfikar Ramzan

Techniques are provided for the detection of malicious software (malware) on a general purpose computing device. A challenge in detecting malicious software is that files are typically scanned for the presence of malicious intent only once (and subsequent rescanning is typically performed in a simplistic manner). Existing methods in the art do not address how to most effectively rescan collections of files in a way that tries to optimize performance and efficacy. These methods may also be useful if additional information is now available regarding a file that might be useful to an end-user or an administrator, even though the file's core disposition might not have changed. More specifically, we describe methods, components, and systems that perform data analytics to intelligently rescan file collections for the purpose of retroactively identifying malware and retroactively identifying clean files.

Method and apparatus for retroactively detecting malicious or otherwise undesirable software as well as clean software through intelligent rescanning

A system retroactively detects malicious software on an end user system without performing expensive cross-referencing directly on the endpoint device. A client provides a server with information about files that are on it together with what it knows about these files. The server tracks this information and cross-references it against new intelligence it gathers on clean or malicious files. If a discrepancy in found (i.e., a file that had been called malicious, but that is actually benign or vice versa), the server informs the client, which in turn takes an appropriate action based on this information.

Method and apparatus for retroactively detecting malicious or otherwise undesirable software

The present invention relates to the security of general purpose computing devices, such as laptop or desktop PCs, and more specifically to the detection of malicious software (malware) on a general purpose computing device. A challenge in detecting malicious software is that files are typically scanned for the presence of malicious intent only once (and subsequent rescanning is typically performed in a simplistic manner). Existing methods in the art do not address how to most effectively rescan collections of files in a way that tries to optimize performance and efficacy. Accordingly we present novel methods, components, and systems for intelligently rescanning file collections and thereby enabling retroactive detection of malicious software and also retroactive identification of clean software. These methods may also be useful if additional information is now available regarding a file that might be useful to an end-user or an administrator, even though the file's core disposition might not have changed. More specifically, we describe methods, components, and systems that perform data analytics to intelligently rescan file collections for the purpose of retroactively identifying malware and retroactively identifying clean files. The disclosed invention provides a significant improvement with regard to efficacy and performance compared to previous approaches.

Novel methods, components, and systems that enhance traditional techniques for detecting malicious software are presented. More specifically, we describe methods, components, and systems that leverage important contextual information from a client system (such as recent history of events on that system) to detect malicious software that might have otherwise gone ignored. The disclosed invention provides a significant improvement with regard to detection capabilities compared to previous approaches.

Method and apparatus for detecting malicious software through contextual convictions

Novel methods, components, and systems for detecting malicious software in a proactive manner are presented. More specifically, we describe methods, components, and systems that leverage machine learning techniques to detect malicious software. The disclosed invention provides a significant improvement with regard to detection capabilities compared to previous approaches.

Method and apparatus for detecting malicious software using machine learning techniques

Novel methods, components, and systems for automatically detecting malicious software are presented. More specifically, methods, components, and systems for the automated deployment of generic signatures to detect malicious software. Even more specifically, computer implemented methods for determining whether a software application is likely malicious including computing at a client component a generic fingerprint for a software application, transmitting the generic fingerprint data to a server component, receiving at the client component information from the server component relating to the generic fingerprint of the software application, and following a prescribed set of actions based on the information received from the server.

Method and apparatus for detecting malicious software using generic signatures

Novel methods, components, and systems that enhance traditional techniques for detecting malicious software are presented. More specifically, methods, components, and systems that use important contextual information from a client system (such as recent history of events on that system), machine learning techniques, the automated deployment of generic signatures, and combinations thereof, to detect malicious software. The disclosed invention provides a significant improvement with regard to automation compared to previous approaches.

Method and apparatus for detecting malicious software through contextual convictions, generic signatures and machine learning techniques

A system for retroactively detecting malicious software on an end user system without performing expensive cross-referencing directly on the endpoint device. A client provides a server with information about files that are on it together with what it knows about these files. The server tracks this information and cross-references it against new intelligence it gathers on clean or malicious files. If a discrepancy is found (i.e., a file that had been called malicious, but that is actually benign or vice versa), the server informs the client, which in turn takes an appropriate action based on this information.

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Calgary, Canada

Alfred Huger