Patents by Inventor Marco SCHWEIGHAUSER
Marco SCHWEIGHAUSER has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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System and method for email account takeover detection and remediation utilizing anonymized datasets
Patent number: 11665195Abstract: A new approach is proposed to support account takeover (ATO) detection based on login attempts by users. The approach relies on assessing fraudulence confidence level of login IP addresses to classify the login attempts by the users. A plurality of attributes/features in one or more user login data logs are extracted and used to build a labeled dataset for training a machine learning (ML) model that relies on statistics of the login attempts to classify and detect fraudulent logins. These attributes make it possible to ascertain if a login attempt or instance by a user is suspicious based on the ML model. In some embodiments, the ML model is trained using anonymized user login data to preserve privacy of the users and a proper level of data anonymization is determined based on the ML model's accuracy in detecting the ATO attacks when trained with different versions of the anonymized data.Type: GrantFiled: November 17, 2020Date of Patent: May 30, 2023Assignee: Barracuda Networks, Inc.Inventors: Mohamed Hosam Afifi Ibrahim, Marco Schweighauser, Asaf Cidon -
Patent number: 11563757Abstract: A new approach is proposed to support account takeover (ATO) detection based on login attempts by users. The approach relies on assessing fraudulence confidence level of login IP addresses to classify the login attempts by the users. A plurality of attributes/features in one or more user login data logs are extracted and used to build a labeled dataset for training a machine learning (ML) model that relies on statistics of the login attempts to classify and detect fraudulent logins. These attributes make it possible to ascertain if a login attempt or instance by a user is suspicious based on the ML model. In some embodiments, the ML model is trained using anonymized user login data to preserve privacy of the users and a proper level of data anonymization is determined based on the ML model's accuracy in detecting the ATO attacks when trained with different versions of the anonymized data.Type: GrantFiled: November 17, 2020Date of Patent: January 24, 2023Assignee: Barracuda Networks, Inc.Inventors: Mohamed Hosam Afifi Ibrahim, Marco Schweighauser, Asaf Cidon
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Patent number: 11356469Abstract: A new approach is proposed to support generating and presenting to a user cyber attack monetary impact estimation of a current or future cyber attack, which is used to stop monetary losses or to mitigate monetary impacts. First, both historic data and real time data on monetary impact of current and/or potential cyber attacks is continuously collected from a plurality of data pools. The collected data is then synchronized, correlated and filtered/cleansed once the data is available to create fidelity among the data from the plurality of data pools. The cyber attack monetary impact is calculated based on the correlated and cleansed data, and is presented to the user along with one or more suggested applications by the user in response to the cyber attack monetary impact, to mitigate the monetary impact of the current or future cyber attack.Type: GrantFiled: June 11, 2020Date of Patent: June 7, 2022Assignee: Barracuda Networks, Inc.Inventors: Alexey Tsitkin, Marco Schweighauser, Nadia Korshun, Shachar Sapir, Fleming Shi
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Patent number: 11159565Abstract: A new approach is proposed that contemplates systems and methods to support email account takeover detection and remediation by utilizing an artificial intelligence (AI) engine/classifier that detects and remediates such attacks in real time. The AI engine is configured to continuously monitor and identify communication patterns of a user on an electronic messaging system of an entity via application programming interface (API) calls. The AI engine is then configured to collect and utilize a variety of features and/or signals from an email sent from an internal email account of the entity. The AI engine combines these signals to automatically detect whether the email account has been compromised by an external attacker and alert the individual user of the account and/or a system administrator accordingly in real time. The AI engine further enables the parties to remediate the effects of the compromised email account by performing one or more remediating actions.Type: GrantFiled: July 16, 2020Date of Patent: October 26, 2021Assignee: Barracuda Networks, Inc.Inventors: Marco Schweighauser, Lior Gavish, Itay Bleier, Asaf Cidon
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Publication number: 20210090816Abstract: A new approach is proposed to support account takeover (ATO) detection based on login attempts by users. The approach relies on assessing fraudulence confidence level of login IP addresses to classify the login attempts by the users. A plurality of attributes/features in one or more user login data logs are extracted and used to build a labeled dataset for training a machine learning (ML) model that relies on statistics of the login attempts to classify and detect fraudulent logins. These attributes make it possible to ascertain if a login attempt or instance by a user is suspicious based on the ML model. In some embodiments, the ML model is trained using anonymized user login data to preserve privacy of the users and a proper level of data anonymization is determined based on the ML model's accuracy in detecting the ATO attacks when trained with different versions of the anonymized data.Type: ApplicationFiled: November 17, 2020Publication date: March 25, 2021Inventors: Mohamed Hosam Afifi Ibrahim, Marco Schweighauser, Asaf Cidon
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SYSTEM AND METHOD FOR EMAIL ACCOUNT TAKEOVER DETECTION AND REMEDIATION UTILIZING ANONYMIZED DATASETS
Publication number: 20210075824Abstract: A new approach is proposed to support account takeover (ATO) detection based on login attempts by users. The approach relies on assessing fraudulence confidence level of login IP addresses to classify the login attempts by the users. A plurality of attributes/features in one or more user login data logs are extracted and used to build a labeled dataset for training a machine learning (ML) model that relies on statistics of the login attempts to classify and detect fraudulent logins. These attributes make it possible to ascertain if a login attempt or instance by a user is suspicious based on the ML model. In some embodiments, the ML model is trained using anonymized user login data to preserve privacy of the users and a proper level of data anonymization is determined based on the ML model's accuracy in detecting the ATO attacks when trained with different versions of the anonymized data.Type: ApplicationFiled: November 17, 2020Publication date: March 11, 2021Inventors: Mohamed Hosam Afifi Ibrahim, Marco Schweighauser, Asaf Cidon -
Publication number: 20200351301Abstract: A new approach is proposed that contemplates systems and methods to support email account takeover detection and remediation by utilizing an artificial intelligence (AI) engine/classifier that detects and remediates such attacks in real time. The AI engine is configured to continuously monitor and identify communication patterns of a user on an electronic messaging system of an entity via application programming interface (API) calls. The AI engine is then configured to collect and utilize a variety of features and/or signals from an email sent from an internal email account of the entity. The AI engine combines these signals to automatically detect whether the email account has been compromised by an external attacker and alert the individual user of the account and/or a system administrator accordingly in real time. The AI engine further enables the parties to remediate the effects of the compromised email account by performing one or more remediating actions.Type: ApplicationFiled: July 16, 2020Publication date: November 5, 2020Inventors: Marco SCHWEIGHAUSER, Lior GAVISH, Itay BLEIER, Asaf CIDON
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Publication number: 20200314137Abstract: A new approach is proposed to support generating and presenting to a user cyber attack monetary impact estimation of a current or future cyber attack, which is used to stop monetary losses or to mitigate monetary impacts. First, both historic data and real time data on monetary impact of current and/or potential cyber attacks is continuously collected from a plurality of data pools. The collected data is then synchronized, correlated and filtered/cleansed once the data is available to create fidelity among the data from the plurality of data pools. The cyber attack monetary impact is calculated based on the correlated and cleansed data, and is presented to the user along with one or more suggested applications by the user in response to the cyber attack monetary impact, to mitigate the monetary impact of the current or future cyber attack.Type: ApplicationFiled: June 11, 2020Publication date: October 1, 2020Applicant: Barracuda Networks, Inc.Inventors: Alexey Tsitkin, Marco Schweighauser, Nadia Korshun, Shachar Sapir, Fleming Shi
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Patent number: 10778717Abstract: A new approach is proposed that contemplates systems and methods to support email account takeover detection and remediation by utilizing an artificial intelligence (AI) engine/classifier that detects and remediates such attacks in real time. The AI engine is configured to continuously monitor and identify communication patterns of a user on an electronic messaging system of an entity via application programming interface (API) calls. The AI engine is then configured to collect and utilize a variety of features and/or signals from an email sent from an internal email account of the entity. The AI engine combines these signals to automatically detect whether the email account has been compromised by an external attacker and alert the individual user of the account and/or a system administrator accordingly in real time. The AI engine further enables the parties to remediate the effects of the compromised email account by performing one or more remediating actions.Type: GrantFiled: March 25, 2019Date of Patent: September 15, 2020Assignee: Barracuda Networks, Inc.Inventors: Marco Schweighauser, Lior Gavish, Itay Bleier, Asaf Cidon
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Publication number: 20190222606Abstract: A new approach is proposed that contemplates systems and methods to support email account takeover detection and remediation by utilizing an artificial intelligence (AI) engine/classifier that detects and remediates such attacks in real time. The AI engine is configured to continuously monitor and identify communication patterns of a user on an electronic messaging system of an entity via application programming interface (API) calls. The AI engine is then configured to collect and utilize a variety of features and/or signals from an email sent from an internal email account of the entity. The AI engine combines these signals to automatically detect whether the email account has been compromised by an external attacker and alert the individual user of the account and/or a system administrator accordingly in real time. The AI engine further enables the parties to remediate the effects of the compromised email account by performing one or more remediating actions.Type: ApplicationFiled: March 25, 2019Publication date: July 18, 2019Inventors: Marco SCHWEIGHAUSER, Lior GAVISH, Itay BLEIER, Asaf CIDON