Patents by Inventor Daniel DICHIU
Daniel DICHIU 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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Patent number: 11847214Abstract: In some embodiments, a behavior classifier comprises a set of neural networks trained to determine whether a monitored software entity is malicious according to a sequence of computing events caused by the execution of the respective entity. When the behavior classifier indicates that the entity is malicious, some embodiments execute a memory classifier comprising another set of neural networks trained to determine whether the monitored entity is malicious according to a memory snapshot of the monitored entity. Applying the classifiers in sequence may substantially reduce the false positive detection rate, while reducing computational costs.Type: GrantFiled: April 21, 2020Date of Patent: December 19, 2023Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Andreea Dincu, Robert M Botarleanu, Sorina N. Zamfir, Elena A Bosinceanu, Razvan Prejbeanu
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Patent number: 11323459Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.Type: GrantFiled: December 10, 2018Date of Patent: May 3, 2022Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Stefan Niculae, Elena A. Bosinceanu, Sorina N. Stoian, Andreea Dincu, Andrei A. Apostoae
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Publication number: 20210326438Abstract: In some embodiments, a behavior classifier comprises a set of neural networks trained to determine whether a monitored software entity is malicious according to a sequence of computing events caused by the execution of the respective entity. When the behavior classifier indicates that the entity is malicious, some embodiments execute a memory classifier comprising another set of neural networks trained to determine whether the monitored entity is malicious according to a memory snapshot of the monitored entity. Applying the classifiers in sequence may substantially reduce the false positive detection rate, while reducing computational costs.Type: ApplicationFiled: April 21, 2020Publication date: October 21, 2021Inventors: Daniel DICHIU, Andreea DINCU, Robert M. BOTARLEANU, Sorina N. ZAMFIR, Elena A. BOSINCEANU, Razvan PREJBEANU
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Patent number: 11153332Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.Type: GrantFiled: December 10, 2018Date of Patent: October 19, 2021Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Stefan Niculae, Elena A. Bosinceanu, Sorina N. Stoian, Andreea Dincu, Andrei A. Apostoae
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Patent number: 11089034Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.Type: GrantFiled: December 10, 2018Date of Patent: August 10, 2021Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Stefan Niculae, Elena A. Bosinceanu, Sorina N. Stoian, Andreea Dincu, Andrei A. Apostoae
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Publication number: 20200186546Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.Type: ApplicationFiled: December 10, 2018Publication date: June 11, 2020Inventors: Daniel DICHIU, Stefan NICULAE, Elena A. BOSINCEANU, Sorina N. STOIAN, Andreea DINCU, Andrei A. APOSTOAE
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Publication number: 20200186545Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.Type: ApplicationFiled: December 10, 2018Publication date: June 11, 2020Inventors: Daniel DICHIU, Stefan NICULAE, Elena A. BOSINCEANU, Sorina N. STOIAN, Andreea DINCU, Andrei A. APOSTOAE
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Publication number: 20200186544Abstract: In some embodiments, a behavioral computer security system protects clients and networks against threats such as malicious software and intrusion. A set of client profiles is constructed according to a training corpus of events occurring on clients, wherein each client profile represents a subset of protected machines, and each client profile is indicative of a normal or baseline pattern of using the machines assigned to the client respective profile. A client profile may group together machines having a similar event statistic. Following training, events detected on a client are selectively analyzed against a client profile associated with the respective client, to detect anomalous behavior. In some embodiments, individual events are analyzed in the context of other events, using a multi-dimensional event embedding space.Type: ApplicationFiled: December 10, 2018Publication date: June 11, 2020Inventors: Daniel DICHIU, Stefan NICULAE, Elena A. BOSINCEANU, Sorina N. STOIAN, Andreea DINCU, Andrei A. APOSTOAE
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Patent number: 10212114Abstract: Described spam detection techniques including string identification, pre-filtering, and frequency spectrum and timestamp comparison steps facilitate accurate, computationally-efficient detection of rapidly-changing spam arriving in short-lasting waves. In some embodiments, a computer system extracts a target character string from an electronic communication such as a blog comment, transmits it to an anti-spam server, and receives an indicator of whether the respective electronic communication is spam or non-spam from the anti-spam server. The anti-spam server determines whether the electronic communication is spam or non-spam according to features of the frequency spectrum of the target string. Some embodiments also perform an unsupervised clustering of incoming target strings into clusters, wherein all members of a cluster have similar spectra.Type: GrantFiled: September 7, 2015Date of Patent: February 19, 2019Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Lucian Z Lupsescu
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Publication number: 20150381539Abstract: Described spam detection techniques including string identification, pre-filtering, and frequency spectrum and timestamp comparison steps facilitate accurate, computationally-efficient detection of rapidly-changing spam arriving in short-lasting waves. In some embodiments, a computer system extracts a target character string from an electronic communication such as a blog comment, transmits it to an anti-spam server, and receives an indicator of whether the respective electronic communication is spam or non-spam from the anti-spam server. The anti-spam server determines whether the electronic communication is spam or non-spam according to features of the frequency spectrum of the target string. Some embodiments also perform an unsupervised clustering of incoming target strings into clusters, wherein all members of a cluster have similar spectra.Type: ApplicationFiled: September 7, 2015Publication date: December 31, 2015Inventors: Daniel DICHIU, Lucian Z. LUPSESCU
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Patent number: 9130778Abstract: Described spam detection techniques including string identification, pre-filtering, and frequency spectrum and timestamp comparison steps facilitate accurate, computationally-efficient detection of rapidly-changing spam arriving in short-lasting waves. In some embodiments, a computer system extracts a target character string from an electronic communication such as a blog comment, transmits it to an anti-spam server, and receives an indicator of whether the respective electronic communication is spam or non-spam from the anti-spam server. The anti-spam server determines whether the electronic communication is spam or non-spam according to features of the frequency spectrum of the target string. Some embodiments also perform an unsupervised clustering of incoming target strings into clusters, wherein all members of a cluster have similar spectra.Type: GrantFiled: January 25, 2012Date of Patent: September 8, 2015Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Lucian Z Lupsescu
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Patent number: 8954519Abstract: Described spam detection techniques including string identification, pre-filtering, and character histogram and timestamp comparison steps facilitate accurate, computationally-efficient detection of rapidly-changing spam arriving in short-lasting waves. In some embodiments, a computer system extracts a target character string from an electronic communication such as a blog comment, transmits it to an anti-spam server, and receives an indicator of whether the respective electronic communication is spam or non-spam from the anti-spam server. The anti-spam server determines whether the electronic communication is spam or non-spam according to certain features of the character histogram of the target string. Some embodiments also perform an unsupervised clustering of incoming target strings into clusters, wherein all members of a cluster have similar character histograms.Type: GrantFiled: January 25, 2012Date of Patent: February 10, 2015Assignee: Bitdefender IPR Management Ltd.Inventors: Daniel Dichiu, Lucian Z. Lupsescu
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Publication number: 20130191468Abstract: Described spam detection techniques including string identification, pre-filtering, and frequency spectrum and timestamp comparison steps facilitate accurate, computationally-efficient detection of rapidly-changing spam arriving in short-lasting waves. In some embodiments, a computer system extracts a target character string from an electronic communication such as a blog comment, transmits it to an anti-spam server, and receives an indicator of whether the respective electronic communication is spam or non-spam from the anti-spam server. The anti-spam server determines whether the electronic communication is spam or non-spam according to features of the frequency spectrum of the target string. Some embodiments also perform an unsupervised clustering of incoming target strings into clusters, wherein all members of a cluster have similar spectra.Type: ApplicationFiled: January 25, 2012Publication date: July 25, 2013Inventors: Daniel DICHIU, Lucian Z. LUPSESCU
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Publication number: 20130191469Abstract: Described spam detection techniques including string identification, pre-filtering, and character histogram and timestamp comparison steps facilitate accurate, computationally-efficient detection of rapidly-changing spam arriving in short-lasting waves. In some embodiments, a computer system extracts a target character string from an electronic communication such as a blog comment, transmits it to an anti-spam server, and receives an indicator of whether the respective electronic communication is spam or non-spam from the anti-spam server. The anti-spam server determines whether the electronic communication is spam or non-spam according to certain features of the character histogram of the target string. Some embodiments also perform an unsupervised clustering of incoming target strings into clusters, wherein all members of a cluster have similar character histograms.Type: ApplicationFiled: January 25, 2012Publication date: July 25, 2013Inventors: Daniel DICHIU, Lucian Z. LUPSESCU