Patents by Inventor Mark Abene

Mark Abene 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).

  • Publication number: 20180121655
    Abstract: A mobile device is made resistant to malware. Wireless mobile devices are paired with short-distance wireless technology to separate user gadgets like keyfobs. Two or more pieces of security passwords are escrowed separately amongst the physically distinct devices. Neither the mobile device nor its matching keyfob store or keep entire passwords.
    Type: Application
    Filed: December 30, 2017
    Publication date: May 3, 2018
    Applicant: Intersections, Inc.
    Inventors: Mark Abene, Mojtaba Cazi, Konstantin Bokarius, Henry Yei
  • Patent number: 9848324
    Abstract: Physical security methods and equipment are applied to mobile devices that use multi-factor authentication mobile apps. Herein, a password management mobile app physically escrows each encrypted password that must be stored into two parts. These are then distributed between two separate, independent physical devices. Only one of those parts is kept only in a separate user gadget like a keyfob. Any reconstitution of each password after decryption requires that the user have on-hand both the mobile device and the separate user gadget. Such reconstitution is one password at a time, and only as needed, and released for use in remote authentication with a master user password entry.
    Type: Grant
    Filed: June 13, 2017
    Date of Patent: December 19, 2017
    Assignee: Intersections Inc.
    Inventors: Mark Abene, Seyed Mojtaba Ghazitabrizi, Konstantin Bokarius, Henry Yei
  • Patent number: 9237164
    Abstract: Provided is an intrusion detection system configured to detect anomalies indicative of a zero-day attack by statistically analyzing substantially all traffic on a network in real-time. The intrusion detection system, in some aspects, includes a network interface; one or more processors communicatively coupled to the network interface; system memory communicatively coupled to the processors. The system memory, in some aspects, stores instructions that when executed by the processors cause the processors to perform steps including: buffering network data from the network interface in the system memory; retrieving the network data buffered in the system memory; applying each of a plurality of statistical or machine-learning intrusion-detection models to the retrieved network data; aggregating intrusion-likelihood scores from each of the intrusion-detection models in an aggregate score, and upon the aggregate score exceeding a threshold, outputting an alert.
    Type: Grant
    Filed: June 19, 2014
    Date of Patent: January 12, 2016
    Assignee: Vectra Networks, Inc.
    Inventors: James Harlacher, Mark Abene
  • Publication number: 20150082433
    Abstract: Provided is an intrusion detection system configured to detect anomalies indicative of a zero-day attack by statistically analyzing substantially all traffic on a network in real-time. The intrusion detection system, in some aspects, includes a network interface; one or more processors communicatively coupled to the network interface; system memory communicatively coupled to the processors. The system memory, in some aspects, stores instructions that when executed by the processors cause the processors to perform steps including: buffering network data from the network interface in the system memory; retrieving the network data buffered in the system memory; applying each of a plurality of statistical or machine-learning intrusion-detection models to the retrieved network data; aggregating intrusion-likelihood scores from each of the intrusion-detection models in an aggregate score, and upon the aggregate score exceeding a threshold, outputting an alert.
    Type: Application
    Filed: June 19, 2014
    Publication date: March 19, 2015
    Applicant: VECTRA NETWORKS, INC.
    Inventors: James Harlacher, Mark Abene
  • Publication number: 20140101763
    Abstract: Provided is an intrusion detection system configured to detect anomalies indicative of a zero-day attack by statistically analyzing substantially all traffic on a network in real-time. The intrusion detection system, in some aspects, includes a network interface; one or more processors communicatively coupled to the network interface; system memory communicatively coupled to the processors. The system memory, in some aspects, stores instructions that when executed by the processors cause the processors to perform steps including: buffering network data from the network interface in the system memory; retrieving the network data buffered in the system memory; applying each of a plurality of statistical or machine-learning intrusion-detection models to the retrieved network data; aggregating intrusion-likelihood scores from each of the intrusion-detection models in an aggregate score, and upon the aggregate score exceeding a threshold, outputting an alert.
    Type: Application
    Filed: October 29, 2012
    Publication date: April 10, 2014
    Applicant: TRACEVECTOR, INC.
    Inventors: James Harlacher, Mark Abene
  • Publication number: 20140101762
    Abstract: Provided is an intrusion detection system configured to detect anomalies indicative of a zero-day attack by statistically analyzing substantially all traffic on a network in real-time. The intrusion detection system, in some aspects, includes a network interface; one or more processors communicatively coupled to the network interface; system memory communicatively coupled to the processors. The system memory, in some aspects, stores instructions that when executed by the processors cause the processors to perform steps including: buffering network data from the network interface in the system memory; retrieving the network data buffered in the system memory; applying each of a plurality of statistical or machine-learning intrusion-detection models to the retrieved network data; aggregating intrusion-likelihood scores from each of the intrusion-detection models in an aggregate score, and upon the aggregate score exceeding a threshold, outputting an alert.
    Type: Application
    Filed: October 29, 2012
    Publication date: April 10, 2014
    Applicant: TRACEVECTOR, INC.
    Inventors: James Harlacher, Mark Abene
  • Publication number: 20140101761
    Abstract: Provided is an intrusion detection system configured to detect anomalies indicative of a zero-day attack by statistically analyzing substantially all traffic on a network in real-time. The intrusion detection system, in some aspects, includes a network interface; one or more processors communicatively coupled to the network interface; system memory communicatively coupled to the processors. The system memory, in some aspects, stores instructions that when executed by the processors cause the processors to perform steps including: buffering network data from the network interface in the system memory; retrieving the network data buffered in the system memory; applying each of a plurality of statistical or machine-learning intrusion-detection models to the retrieved network data; aggregating intrusion-likelihood scores from each of the intrusion-detection models in an aggregate score, and upon the aggregate score exceeding a threshold, outputting an alert.
    Type: Application
    Filed: October 9, 2012
    Publication date: April 10, 2014
    Inventors: James Harlacher, Mark Abene