Flying Cloud Technologies, Inc.

Zettaset, Inc.

Other

Brian P Christian

Michael J Cramer

Michael Dalton

Michael A Ware

Ruth Christian

Timothy Demarest

Data surveillance techniques are presented for the detection of security and/or performance issues on a zero-trust computer network. There is a network device policy manager that works in conjunction with a network data policy manager and which is in charge of performing the above data surveillance. Of special interest are those security issues where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network but can impersonate themselves as legitimate users. The above data surveillance techniques are also applied for detecting intentional or unintentional exfiltration/leak of privileged data/assets between unauthorized users/groups of the organization.

Data surveillance in a zero-trust network

Techniques are disclosed for identifying anomalous subjects and devices at a site. The devices may or may not be carried by or associated with subjects at the site. A number of various types of sensors may be utilized for this purpose. The sensors gather data about the subjects and devices. The data is processed by a data processing module which provides its output to a rolling baseline engine. The rolling baseline engine establishes a baseline for what is considered the 'normal' behavior for subjects/devices at the site based on a desired dimension of analysis. Data associated with subjects/devices that is not normal is identified as an anomaly along with the associated subject/device. The findings are archived for performing analytics as required.

Anomalous subject and device identification based on rolling baseline

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. The above data surveillance techniques are also applied for detecting intentional or unintentional exfiltration/leak of privileged data/assets between unauthorized users/groups of the organization. Such detection may be performed based on analyzing threat stream data from threat intelligence providers.

Data surveillance for privileged assets based on threat streams

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. The above data surveillance techniques are also applied for detecting intentional or unintentional exfiltration/leak of privileged data/assets between unauthorized users/groups of the organization.

Data surveillance for privileged assets on a computer network

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. A centroid representing the normal population of the data packets is identified. The design allows establishing the context of various events of interest in the organization, thus enabling dynamic management of security policies.

Data surveillance system with contextual information

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. A centroid representing the normal population of the data packets is identified. The movement or drift of the centroid in response to various events is measured and analyzed. This allows the system to evolve its baseline over time thereby preventing issuing false positives for such events.

Data surveillance system with patterns of centroid drift

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. The design incorporates deployment in a distributed network so that the devices of the network participate in the detection of anomalies as a community.

Distributed data surveillance in a community capture environment

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. A set of metadata layers related to the analysis are also maintained. The techniques are also applicable for detecting performance issues indicative of a system malfunction or deterioration.

Data surveillance system

Data surveillance techniques are presented for the detection of security issues, especially of the kind where privileged data may be stolen by steganographic, data manipulation or any form of exfiltration attempts. Such attempts may be made by rogue users or admins from the inside of a network, or from outside hackers who are able to intrude into the network and impersonate themselves as legitimate users. The system and methods use a triangulation process whereby analytical results pertaining to data protocol, user-behavior and packet content are combined to establish a baseline for the data. Subsequent incoming data is then scored and compared against the baseline to detect any security anomalies. The techniques are also applicable for detecting performance issues indicative of a system malfunction or deterioration.

A system and a method for spill management during the shuffle phase of a map-reduce job performed in a distributed computer system on distributed files. A spilling protocol is provided for handling the spilling of intermediate data based on at least one popularity attribute of key-value pairs of the input data on which the map-reduce job is performed. The spilling protocol includes an assignment order to storage resources belonging to the computer system based on the at least one popularity attribute. The protocol can be deployed in computer systems with heterogeneous storage resources. Additionally, pointers or tags can be assigned to improve shuffle phase performance. The distributed file systems that are most suitable are ones usable by Hadoop, e.g., Hadoop Distributed File System (HDFS).

Management of intermediate data spills during the shuffle phase of a map-reduce job

A method and high availability cluster configuration for capture of data packets propagating in an external network into the cluster through a wired-tap connection between the cluster and at least one router of the external network. Captured packets are supplied with popularity tags and striped over high-access-rate non-transitory storage media belonging to a capture pool of serving nodes that are specially provisioned and selected by the master node. The captured packets are either devolved or demoted to low-access-rate non-transitory storage media of at least two serving nodes not including any backup master based on popularity metrics contained their tags. The demotion, devolution as well as determination of popularity metrics and setting of a popularity threshold are managed by a distributed management protocol.

Capturing data packets from external networks into high availability clusters while maintaining high availability of popular data packets

A roller cover washer for removing paint from a paint roller cover. The roller cover washer includes a roller housing having a front portion and a rear portion. The front portion has a front wall, a rear wall and a bottom wall for defining an interior space therebetween. The interior space is for receiving a paint roller cover. At least one of a fluid inlet apertures are in fluid communication with the interior space. A plurality of fluid outlet apertures through the front wall. The rear portion is coupled to the rear wall. A fluid connector is in fluid communication with the fluid inlet apertures. A motor housing is releasably coupled along a top edge of the roller housing. The motor housing has a motor. A switch is operationally coupled to the motor for controlling the motor. A cover retainer is for securing the roller cover within the inner space of the roller housing. The cover retainer is operationally coupled to the motor. A bracket is coupled to the rear wall of the roller housing.

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Sioux Falls, SD, United States of America

Brian P Christian