Centripetal Networks, Inc.

Shower Solutions, LLC

Other

Avaya Inc.

Centripetal LLC

Sean A Moore

Peter P Geremia

David K Ahn

Jonathan R Rogers

Steven Rogers

Vincent Mutolo

Robert T Perry

Pierre Mallett, Iii

Keith A George

Jess P Parnell

Douglas M DiSabello

Richard Goodwin

Alexander Chinchilli

Paul Sprague

Connor Tess

Matthew Sparrow

John W Fenton

John Daniel Scoggins, Sr

Zachary Ehnerd

Patrick Gwen

Justin Makoto Leach

Christopher T Rodney

Paul Roller Michaelis

David L Chavez

Ilya Freytsis

Scott Ball

Lee Joseph Wilson

Joseph Greenwald

David J Montana

Howard G Kradjel

Jos&eacute; Luis Herrero

John Daniel Scoggins

John Fenton

Pierre Mallett Iii

Douglas Disabello

Douglas M Disabello

Iii Pierre Mallett

John Daniel Scoggins Sr

Jess Parnell

The attack vectors for some denial-of-service cyber attacks on the Internet's Domain Name System (DNS) are bad, bogus, or unregistered domain name DNS requests to resolve domain names that are not registered in the DNS. Some other cyber attacks steal sensitive data by encoding the data in bogus domain names, or domain names otherwise not registered in the DNS, that are transferred across networks in bogus DNS requests. A DNS gatekeeper may filter in-transit packets containing DNS requests and may efficiently determine if a request's domain name is registered in the DNS. When the domain name is not registered in the DNS, the DNS gatekeeper may take one of a plurality of protective actions. The DNS gatekeeper drops requests determined not to be legitimate, which may prevent an attack.

Methods and systems for prevention of attacks associated with the domain name system

A packet-filtering system configured to filter packets in accordance with packet-filtering rules may receive data indicating network-threat indicators and may configure the packet-filtering rules to cause the packet-filtering system to identify packets comprising unencrypted data, and packets comprising encrypted data. A portion of the unencrypted data may correspond to one or more of the network-threat indicators, and the packet-filtering rules may be configured to cause the packet-filtering system to determine, based on the portion of the unencrypted data, that the packets comprising encrypted data correspond to the one or more network-threat indicators.

Rule-based network-threat detection for encrypted communications

In some variations, first and second rule sets may be received by a network protection device. The first and second rule sets may be preprocessed. The network protection device may be configured to process packets in accordance with the first rule set. Packets may be received by the network protection device. A first portion of the packets may be processed in accordance with the first rule set. The network protection device may be reconfigured to process packets in accordance with the second rule set. A second portion of the packets may be processed in accordance with the second rule set.

Rule swapping in a packet network

Packets may be received by a packet security gateway. Responsive to a determination that an overload condition has occurred in one or more networks associated with the packet security gateway, a first group of packet filtering rules may be applied to at least some of the packets. Applying the first group of packet filtering rules may include allowing at least a first portion of the packets to continue toward their respective destinations. Responsive to a determination that the overload condition has been mitigated, a second group of packet filtering rules may be applied to at least some of the packets. Applying the second group of packet filtering rules may include allowing at least a second portion of the packets to continue toward their respective destinations.

Protecting networks from cyber attacks and overloading

Methods and systems are disclosed for integrating cyber threat intelligence (CTI), threat metadata, and threat intelligence gateways with analysis systems to form efficient and effective system for active, proactive, and reactive network protection. A network gateway may be composed of multiple stages. A first stage may include a threat intelligence gateway (TIG). A second stage may include one or more cyber analysis systems that ingest TIG-filtered communications and associated threat metadata signals. A third stage may include network protection logic that determines which protective actions. The gateway may be provisioned and configured with rules that specify the network protection policies to be enforced. The gateway may ingest all communications flowing between the protected network and the unprotected network.

Methods and systems for efficient network protection

SSL/TLS certificate filtering devices, systems and processes may filter packets based on risk associated with each packet. A risk score may be determined for each packet based on associated threats and risks. Risk scores may be determined based on certificates, certificate authorities, and/or end users associated with each packet. The certificates may be scored and/or categorized by threats and risk.

Methods and systems for certificate filtering

Methods, systems, and computer-readable media for efficiently detecting threat incidents for cyber threat analysis are described herein. In various embodiments, a computing device, which may be located at a boundary between a protected network associated with the enterprise and an unprotected network, may combine one or more threat indicators received from one or more threat intelligence providers; may generate one or more packet capture and packet filtering rules based on the combined threat indicators; and, may capture or filter, on a packet-by-packet basis, at least one packet based on the generated rules. In other embodiments, a computing device may generate a packet capture file comprising raw packet content and corresponding threat context information, wherein the threat context information may comprise a filtering rule and an associated threat indicator that caused the packet to be captured.

Efficient packet capture for cyber threat analysis

Methods, apparatuses, systems, and machine-readable media are disclosed for improving packet filtering efficiency by reducing processing time and/or by reducing memory usage. Any of various types of data structures, such as flat hash maps and/or ruletrees, may be used by a packet filtering appliance to search for cybersecurity policy packet filtering rules that should be applied to in-transit packets. The packet filtering appliance may search the index data structures for matches of search objects, in the form of values that the packet filtering appliance extracts from in-transit packets, to threat indicator matching criteria of the policy rules. Each of the index data structures may map rule identifiers (rule IDs) of policy rules to keys that are based on (or that comprise) the matching criteria of those rules.

Methods and systems for efficient cybersecurity policy enforcement on network communications

Malicious homoglyphic domain name (MHDN) generation and associated cyber security applications are described. MHDN generation may be performed by, for example, generating, based on training data, a set of operations for use in generating the one or more potential MHDNs, wherein each operation of the set of operations may be configured to modify a base domain name according to a respective homoglyphic characteristic. The set of operations may be used to generate one or more candidate MHDN mutators. The candidate MHDN mutators may be tested for fitness values corresponding to respective likelihoods of generating an MHDN and the candidate MHDN mutators may be applied to one or more base domain names to generate potential MHDNs.

Malicious homoglyphic domain name generation and associated cyber security applications

A packet-filtering system described herein may be configured to filter packets with encrypted hostnames in accordance with one or packet-filtering rules. The packet-filtering system may resolve a plaintext hostname from ciphertext comprising an encrypted Server Name Indication (eSNI) value. The packet-filtering system may resolve the plaintext hostname using a plurality of techniques. Once the plaintext hostname is resolved, the packet-filtering system may then use the plaintext hostname to determine whether the packets are associated with one or more threat indicators. If the packet-filtering system determines that the packets are associated with one or more threat indicators, the packet-filtering system may apply a packet filtering operation associated with the packet-filtering rules to the packets.

Methods and systems for efficient encrypted SNI filtering for cybersecurity applications

A threat intelligence gateway (TIG) may protect TCP/IP networks from network (e.g., Internet) threats by enforcing certain policies on in-transit packets that are crossing network boundaries. The policies may be composed of packet filtering rules with packet-matching criteria derived from cyber threat intelligence (CTI) associated with Internet threats. These CTI-derived packet-filtering rules may be created offline by policy creation and management servers, which may distribute the policies to subscribing TIGs that subsequently enforce the policies on in-transit packets. Each packet filtering rule may specify a disposition that may be applied to a matching in-transit packet, such as deny/block/drop the in-transit packet or pass/allow/forward the in-transit packet, and also may specify directives that may be applied to a matching in-transit packet, such as log, capture, spoof-tcp-rst, etc. Often, however, the selection of a rule's disposition and directives that best protect the associated network may not be optimally determined before a matching in-transit packet is observed by the associated TIG. In such cases, threat context information that may only be available (e.g., computable) at in-transit packet observation and/or filtering time, such as current time-of-day, current TIG/network location, current TIG/network administrator, the in-transit packet being determined to be part of an active attack on the network, etc., may be helpful to determine the disposition and directives that may best protect the network from the threat associated with the in-transit packet. The present disclosure describes examples of methods, systems, and apparatuses that may be used for efficiently determining (e.g., accessing and/or computing), in response to the in-transit packet, threat context information associated with an in-transit packet. The threat context information may be used to efficiently determine the disposition and/or one or more directives to apply to the in-transit packet. This may result in dispositions and/or directives being applied to in-transit packets that better protect the network as compared with solely using dispositions and directives that were predetermined prior to receiving the in-transit packet.

Efficient threat context-aware packet filtering for network protection

Malicious homoglyphic domain name (MHDN) detection and associated cyber security applications are described. A domain name may be received that may be a potential MHDN. Homoglyphic domain name detection may be performed by, for example, generating a normalized character string corresponding to the input domain name by applying one or more normalization operations to the input domain name, wherein the one or more normalization operations may be configured to reduce homoglyphic characteristics in the input domain name; and generating a plurality of segmentations of the normalized character string, wherein generating each segmentation, of the plurality of segmentations, may comprise segmenting the normalized character string into a respective plurality of segments, and wherein each segmentation may comprise a different plurality of segments. A segmentation may be selected based on cost values corresponding to each respective segmentation determined using a cost function. The received domain name may be determined to be a homoglyphic domain name based on a determination that one or more segments of the selected first segmentation match a base of a known domain name in the at least one list of known domain names.

Malicious homoglyphic domain name detection and associated cyber security applications

An enterprise organization may operate a central network and one or more remote networks, each comprising a plurality of computing devices. For protection against malicious actors, the central network may be configured to filter network traffic associated with the computing devices based on identified threats. Traffic corresponding to computing devices connected to the remote network may be tunneled to the central network for filtering by the central network. A tunnel gateway device, associated with the remote network, may efficiently identify which communications are associated with Internet threats, and tunnel such identified traffic to the central network, where actions may be taken to protect the enterprise network.

Cyber protections of remote networks via selective policy enforcement at a central network

Growing community of inventors

Houston, TX, United States of America

Sean A Moore