“Rob was the lead in building the infrastructure, visualization, and reporting for BetterWorks analytics. BetterWorks was a data driven organization using objective and key results, weekly reporting, and analysis of every part of the user behavior. Rob was the person leading, executing, and communicating insights across the organization. He was highly efficient, effective, and I highly recommend him for any role.”
San Francisco, California, United States
Contact Info
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Activity
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I write this with a heavy heart. Yesterday, Crunchbase went through a strategic restructuring, which, unfortunately, led to the layoff of several…
I write this with a heavy heart. Yesterday, Crunchbase went through a strategic restructuring, which, unfortunately, led to the layoff of several…
Liked by Robert Conrad
Experience & Education
Patents
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Systems and methods for using file paths to identify potentially malicious computer files
Issued US US8769685 B1
A computer-implemented method for using file paths to identify potentially malicious computer files may include: 1) identifying a file, 2) identifying a file path associated with the file, 3) determining, by applying a heuristic to the file, that at least a portion of the file path is likely to have been randomly generated, 4) determining, based at least in part on the determination that at least portion of the file path has likely been randomly generated, that the file is potentially…
A computer-implemented method for using file paths to identify potentially malicious computer files may include: 1) identifying a file, 2) identifying a file path associated with the file, 3) determining, by applying a heuristic to the file, that at least a portion of the file path is likely to have been randomly generated, 4) determining, based at least in part on the determination that at least portion of the file path has likely been randomly generated, that the file is potentially malicious, and 5) performing a security operation on the file. Corresponding systems and computer-readable instructions embodied on computer-readable media are also disclosed.
Other inventorsSee patent -
Proactively analyzing binary files from suspicious sources
Issued US US8370942 B1
A malware source analysis component determines which sources of malware are sufficiently suspicious such that all binary files located thereon should be analyzed. In order to makes such determinations, the malware source analysis component receives information concerning malware infections from a plurality of sources. The malware source analysis component analyzes the received information, and determines suspiciousness levels associated with specific sources. Responsive to identifying a given…
A malware source analysis component determines which sources of malware are sufficiently suspicious such that all binary files located thereon should be analyzed. In order to makes such determinations, the malware source analysis component receives information concerning malware infections from a plurality of sources. The malware source analysis component analyzes the received information, and determines suspiciousness levels associated with specific sources. Responsive to identifying a given threshold suspiciousness level associated with a source, the malware source analysis component adjudicates that source to be suspicious. Where a source is adjudicated to be suspicious, the malware source analysis component submits submission instructions to that source, directing it to identify binary files thereon and submit them to be analyzed. The malware source analysis component receives binary files from suspicious sources according to the submission instructions, and analyzes the received binary files.
Other inventorsSee patent -
Method and system for employing user input for website classification
Issued US US8364776 B1
A method and apparatus for employing user input to classify unknown websites whereby when a given unknown/unclassified website is accessed on a given computer system associated with a given user, the given user is asked to provide input regarding the legitimacy of the given unknown/unclassified website. The classification of the given unknown/unclassified website is then determined via one or more sources other than the given user's input. The accuracy of the given user's input regarding the…
A method and apparatus for employing user input to classify unknown websites whereby when a given unknown/unclassified website is accessed on a given computer system associated with a given user, the given user is asked to provide input regarding the legitimacy of the given unknown/unclassified website. The classification of the given unknown/unclassified website is then determined via one or more sources other than the given user's input. The accuracy of the given user's input regarding the legitimacy of the given unknown/unclassified website is then determined and used to calculate, and/or transform, a reliability score that is then associated with the given user. A given user's reliability score is then used to determine the given user's eligibility to provide further input regarding the legitimacy of other unknown/unclassified websites, and/or to determine a value to be place on, or otherwise filter, future input from the given user regarding the legitimacy of other unknown/unclassified websites.
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Using file prevalence to inform aggressiveness of behavioral heuristics
Issued US US8302194 B2
The prevalence rate of a file to be subject to behavior based heuristics analysis is determined, and the aggressiveness level to use in the analysis is adjusted, responsive to the prevalence rate. The aggressiveness is set to higher levels for lower prevalence files and to lower levels for higher prevalence files. Behavior based heuristics analysis is applied to the file, using the set aggressiveness level. In addition to setting the aggressiveness level, the heuristic analysis can also…
The prevalence rate of a file to be subject to behavior based heuristics analysis is determined, and the aggressiveness level to use in the analysis is adjusted, responsive to the prevalence rate. The aggressiveness is set to higher levels for lower prevalence files and to lower levels for higher prevalence files. Behavior based heuristics analysis is applied to the file, using the set aggressiveness level. In addition to setting the aggressiveness level, the heuristic analysis can also comprise dynamically weighing lower prevalence files as being more likely to be malicious and higher prevalence files as being less likely. Based on the applied behavior based heuristics analysis, it is determined whether or not the file comprises malware. If it is determined that the file comprises malware, appropriate steps can be taken, such as blocking, deleting, quarantining and/or disinfecting the file.
Other inventorsSee patent -
Methods and systems for evaluating the health of computing systems based on when operating-system changes occur
Issued US US8281403 B1
A computer-implemented method for evaluating the health of computing systems based on when operating-system changes occur is disclosed. In one example, this method may include: 1) identifying an operating-system change made to a computing system, 2) determining when the operating-system change occurred, and then 3) assessing the health of the computing system based at least in part on when the operating-system change occurred. Various other methods, systems, and computer-readable media are also…
A computer-implemented method for evaluating the health of computing systems based on when operating-system changes occur is disclosed. In one example, this method may include: 1) identifying an operating-system change made to a computing system, 2) determining when the operating-system change occurred, and then 3) assessing the health of the computing system based at least in part on when the operating-system change occurred. Various other methods, systems, and computer-readable media are also disclosed.
Other inventorsSee patent -
Method and system for employing user input for file classification and malware identification
Issued US US8060577 B1
A method and apparatus for employing user input to classify unknown files whereby when a given unknown/unclassified file is downloaded and/or activated on a given computer system associated with a given user, the given user is asked to provide input regarding the legitimacy of the given unknown/unclassified file. The classification of the given unknown/unclassified file is then determined via one or more sources other than the given user's input. The accuracy of the given user's input regarding…
A method and apparatus for employing user input to classify unknown files whereby when a given unknown/unclassified file is downloaded and/or activated on a given computer system associated with a given user, the given user is asked to provide input regarding the legitimacy of the given unknown/unclassified file. The classification of the given unknown/unclassified file is then determined via one or more sources other than the given user's input. The accuracy of the given user's input regarding the legitimacy of the given unknown/unclassified file is then determined and used to calculate, and/or transform, a reliability score that is then associated with the given user. A given user's reliability score is then used to determine the given user's eligibility to provide further input regarding the legitimacy of other unknown/unclassified files, and/or to determine a value to be place on, or otherwise filter, future input from the given user regarding the legitimacy of other unknown/unclassified files.
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Identifying originators of malware
Filed US US8321935 B1
A malware analysis component receives information concerning malware infections on a large plurality of client computers, as detected by an anti-malware product or submitted directly by users. The malware analysis component analyzes this wide array of information, and identifies suspicious malware detection and submission activity associated with specific sources. Where identified suspicious patterns of malware detection and submission activity associated with a specific source meet a given…
A malware analysis component receives information concerning malware infections on a large plurality of client computers, as detected by an anti-malware product or submitted directly by users. The malware analysis component analyzes this wide array of information, and identifies suspicious malware detection and submission activity associated with specific sources. Where identified suspicious patterns of malware detection and submission activity associated with a specific source meet a given threshold over time, the malware analysis component determines that the source is an originator of malware.
Other inventorsSee patent -
Filtering malware related content
Filed US US8302191 B1
A submission filtering component filters malware related content received for analysis. The submission filtering component determines an analysis priority rating for each source from which malware related content is received. An analysis priority ratings is based on various factors indicative of how likely the source is to transmit malware related content that is important to analyze. The malware filtering component transforms the received stream of malware related content into a subset to be…
A submission filtering component filters malware related content received for analysis. The submission filtering component determines an analysis priority rating for each source from which malware related content is received. An analysis priority ratings is based on various factors indicative of how likely the source is to transmit malware related content that is important to analyze. The malware filtering component transforms the received stream of malware related content into a subset to be analyzed, based on the analysis priority ratings associated with sources from which malware related content is received. A malware analysis component analyzes the subset of malware related content.
Other inventorsSee patent
Honors & Awards
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Standing Ovation - Innovation
Joseph Chen, Dir. SQA Engineering
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Standing Ovation - Action
Adam Bromwich, Sr. Dir. Development
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Standing Ovation - Action
Joseph Chen, Sr. Mgr. SQA Engineering
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Standing Ovation - Action
Brad Allen, Sr. Dir. Software Engineering
Symantec’s success depends on employees who embody our values: Innovation, Action, Customer-Driven, and Trust. The Symantec Applause program enables employees to recognize their co-workers for a job well done. The highest award level – the Standing Ovation award – honors outstanding achievements across Symantec.
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Rookie Innovator of the Year
Symantec CTO Organization
This award recognizes extraordinary, high potential innovators with technical contributions from 0-2 years who have gone above and beyond in their engineering efforts.
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A++ Award
Joseph Chen, Mgr. SQA Engineering
This award certifies that Robert Conrad has gone above and beyond the call of duty at Symantec and is being honored with this award of excellence.
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English
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Join now to viewMore activity by Robert
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Congrats to the Crunchbase team on raising their $50M Series D! Thrilled to see their vision come to life over the last 5 years, as jager mcconnell…
Congrats to the Crunchbase team on raising their $50M Series D! Thrilled to see their vision come to life over the last 5 years, as jager mcconnell…
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I am so excited to announce that Crunchbase just closed a $50M Series D round! Everything I’ve worked on with my team over the last year has been in…
I am so excited to announce that Crunchbase just closed a $50M Series D round! Everything I’ve worked on with my team over the last year has been in…
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I am incredibly thankful to the team at Crunchbase for taking a stand through your ACTIONS on committing to building a DIVERSE, EQUAL, INCLUSIVE &…
I am incredibly thankful to the team at Crunchbase for taking a stand through your ACTIONS on committing to building a DIVERSE, EQUAL, INCLUSIVE &…
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Excited to announce that FriendlyData is joining forces with ServiceNow, the world’s most innovative company. We would like to say thank you to our…
Excited to announce that FriendlyData is joining forces with ServiceNow, the world’s most innovative company. We would like to say thank you to our…
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Planning your day at at Collision? Don't miss Crunchbase CEO and industry veteran Jager McConnell at 10:15 AM at the Startup University Stage!
Planning your day at at Collision? Don't miss Crunchbase CEO and industry veteran Jager McConnell at 10:15 AM at the Startup University Stage!
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