Centripetal Networks, LLC

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.

A computing system may identify packets received by a network device from a host located in a first network and may generate log entries corresponding to the packets received by the network device. The computing system may identify packets transmitted by the network device to a host located in a second network and may generate log entries corresponding to the packets transmitted by the network device. Utilizing the log entries corresponding to the packets received by the network device and the log entries corresponding to the packets transmitted by the network device, the computing system may correlate the packets transmitted by the network device with the packets received by the network device.

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.

Aspects described herein may relate to cyber threat detection based on threat context and/or threat changes. Cyber threat intelligence (CTI) data may be received from a CTI provider. Endpoint data that indicates evidence that endpoints are cyber threats may be determined based on the CTI data. The endpoint data may be analyzed and/or compared to stored data associated with the endpoint. The analysis and/or comparison may be performed to determine whether evidence that the endpoint is a cyber threat has changed. Based on any changes, dispositions for the endpoint may be determined and sent. The dispositions may change how devices filter network traffic associated with the endpoint. Alternatives to default dispositions may be determined based on a impact of blocking potentially legitimate network traffic to and/or from the endpoints. Machine-learning models may assist in processing and analyzing CTI data, performing threat monitoring, and/or determining feeds that include the dispositions.

A packet-filtering network appliance such as a threat intelligence gateway (TIG) protects TCP/IP networks from Internet threats by enforcing certain policies on in-transit packets that are crossing network boundaries. The policies are composed of packet filtering rules derived from cyber threat intelligence (CTI). Logs of rule-matching packets and their associated flows are sent to cyberanalysis applications located at security operations centers (SOCs) and operated by cyberanalysts. Some cyber threats/attacks, or incidents, are composed of many different flows occurring at a very high rate, which generates a flood of logs that may overwhelm computer, storage, network, and cyberanalysis resources, thereby compromising cyber defenses. The present disclosure describes incident logging, in which a single incident log efficiently incorporates the logs of the many flows that comprise the incident, thereby potentially reducing resource consumption while improving the informational/cyberanalytical value of the incident log for cyberanalysis when compared to the component flow logs. Incident logging vs. flow logging can be automatically and adaptively switched on or off depending on the combination of resource consumption and informational/cyberanalytical value.

Systems, methods, and apparatuses are described for detection and/or analysis of cyber threats based on updated cyber threat intelligence associated with cyber threats. Packet filtering output data such as logs of packet communications and/or copies of packets may be generated based on first cyber threat intelligence associated with a cyber threat. Updated criteria based on subsequent updated cyber threat intelligence may then be applied to the packet filtering output data.

Aspects described herein may relate to cyber threat detection based on threat context and/or threat changes. Cyber threat intelligence (CTI) data may be received from a CTI provider. Endpoint data that indicates evidence that endpoints are cyber threats may be determined based on the CTI data. The endpoint data may be analyzed and/or compared to stored data associated with the endpoint. The analysis and/or comparison may be performed to determine whether evidence that the endpoint is a cyber threat has changed. Based on any changes, dispositions for the endpoint may be determined and sent. The dispositions may change how devices filter network traffic associated with the endpoint. Alternatives to default dispositions may be determined based on a impact of blocking potentially legitimate network traffic to and/or from the endpoints. Machine-learning models may assist in processing and analyzing CTI data, performing threat monitoring, and/or determining feeds that include the dispositions.

Methods and systems for protecting a secured network are presented. For example, one or more packet security gateways may be associated with a security policy management server. At each packet security gateway, a dynamic security policy may be received from the security policy management server, packets associated with a network protected by the packet security gateway may be received, and at least one of multiple packet transformation functions specified by the dynamic security policy may be performed on the packets.

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.

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.