Abstract: A system and a method for providing cybersecurity services in dual-stack traffic processing within one or more communication networks are disclosed. The system comprises one or more carrier edge nodes to transmit traffic data packets between one or more communication devices and one or more network services within the one or more communication networks by translating Internet Protocol version 6 (IPv6) addresses to Internet Protocol version 4 (IPv4) addresses. The one or more carrier edge nodes configured to assign one or more virtual local area network (VLAN) tags to at least one of: outbound communication device-originated data, and inbound communication device-originated data for identifying the traffic data packets. The one or more carrier edge nodes segments at least one of: the outbound and the inbound communication device-originated data into one or more categories for dynamically enforcing one or more security policies to provide the cybersecurity services.

Abstract: A system and a method for providing trustworthy access enforcement to one or more microservice container images on one or more orchestration platforms is disclosed. The system generates the one or more microservice container images along with pre-defined hardcoded elements. The system creates a pre-defined hash data and store in one or more key management databases to analyze authenticity and integrity of the one or more orchestration platforms. The system executes the one or more microservice container images on the one or more orchestration platforms for obtaining metadata associated with the one or more orchestration platforms at a time of execution of the one or more microservice container images. The system generates entitlement signature secret (ESS) data of the one or more orchestration platforms to compare with the pre-defined hash data for trustworthy access enforcement of the one or more microservice container images on the one or more orchestration platforms.

Abstract: A system and a method for providing peer-to-peer virtual private network (VPN) connections in multi-gateway enterprise networks are disclosed. The system comprises a cloud interaction subsystem, a profile generation subsystem, a network traffic framework subsystem, and a tunnel proxy subsystem. The cloud interaction subsystem obtains at least one of: routing data and one or more encryption keys from the multi-gateway enterprise networks to store in one or more databases. The profile generation subsystem generates a VPN profile for each user of one or more users to connect with an associated enterprise network within the multi-gateway enterprise networks. The network traffic framework subsystem encapsulates and decapsulates outgoing network traffic data and incoming network traffic data respectively.

Abstract: An artificial intelligence (AI)-based system for detecting malware in endpoint devices using a multi-source data fusion and method thereof are disclosed. The AI-based system collects at least one of: endpoint visibility data, publicly available information, and sandbox analysis data using the multi-source data fusion. By using the collected data, the AI-based system generates one or more numerical embeddings for at least one of: one or more files and one or more applications of each endpoint device using one or more AI models. The AI-based system is configured with the one or more AI models to generate one or more dynamic directed graphs based on the generated one or more numerical embeddings to compute a maliciousness risk score. The maliciousness risk score of at least one of the: one or more files and one or more applications is used for detecting the malware in the one or more endpoint devices.

Abstract: A system and method for detecting unauthorized relocation of endpoint devices are disclosed. The system comprises one or more network security appliances and one or more servers. The one or more network security appliances is configured to collect network environment data associated with each endpoint device of the one or more endpoint devices. The one or more servers is configured to obtain the network environment data from the one or more network security appliances for generating one or more hash values for the network environment data of each endpoint device of the one or more endpoint devices. The system performs a comparative analysis between the one or more hash values to generate a Paranoia-Diff-Score (PDS) for each endpoint device of the one or more endpoint devices to detect the unauthorized relocation of the one or more endpoint devices.

Abstract: A processor coupled to a NAND memory device comprising an n by m array of dies having n channels performs error recovery message scheduling and read error recovery on the dies by receiving indications of read errors responsive to attempted execution of a read command on a destination die and creates an error recovery message or instruction in response to the indication. The processor determines the destination die of the error recovery message and sends the error recovery message to a die queue based on the determined destination die. The n×m die queues can each be further divided into p priority queues, and error recovery messages are sent to the appropriate die priority queue based on a priority associated with the error recovery message. The processor fetches error recovery messages from a head of each die priority queue and performs read error recovery at the destination die.

Abstract: A system for detecting anomalous network patterns based on analyzing network traffic data and method thereof are disclosed. The system comprises a flow aggregator subsystem, a flow feature generation subsystem, an anomaly detection subsystem, a dynamic score subsystem, and a calibration subsystem. The system is configured to receive the network traffic data from one or more network endpoints for aggregating the network traffic data into network flow data. The system is configured to generate one or more flow features for each packet associated with the aggregated network traffic data based on a rolling window-based analysis of the one or more attributes. The system is configured to analyze the one or more flow features by utilizing one or more deep-learning models to detect the one or more anomalous network patterns.

Abstract: A processor coupled to a NAND memory device comprising an n by m array of dies having n channels performs error recovery message scheduling and read error recovery on the dies by receiving indications of read errors responsive to attempted execution of a read command on a destination die and creates an error recovery message or instruction in response to the indication. The processor determines the destination die of the error recovery message and sends the error recovery message to a die queue based on the determined destination die. The n×m die queues can each be further divided into p priority queues, and error recovery messages are sent to the appropriate die priority queue based on a priority associated with the error recovery message. The processor fetches error recovery messages from a head of each die priority queue and performs read error recovery at the destination die.

Abstract: A processor coupled to a NAND memory device comprising an n by m array of dies having n channels performs error recovery message scheduling and read error recovery on the dies by receiving indications of read errors responsive to attempted execution of a read command on a destination die and creates an error recovery message or instruction in response to the indication. The processor determines the destination die of the error recovery message and sends the error recovery message to a die queue based on the determined destination die. The n×m die queues can each be further divided into p priority queues, and error recovery messages are sent to the appropriate die priority queue based on a priority associated with the error recovery message. The processor fetches error recovery messages from a head of each die priority queue and performs read error recovery at the destination die.

Abstract: A processor coupled to a NAND memory device comprising an n by m array of dies having n channels performs error recovery message scheduling and read error recovery on the dies by receiving indications of read errors responsive to attempted execution of a read command on a destination die and creates an error recovery message or instruction in response to the indication. The processor determines the destination die of the error recovery message and sends the error recovery message to a die queue based on the determined destination die. The n×m die queues can each be further divided into p priority queues, and error recovery messages are sent to the appropriate die priority queue based on a priority associated with the error recovery message. The processor fetches error recovery messages from a head of each die priority queue and performs read error recovery at the destination die.

Abstract: A processor coupled to an AIPR-enabled NAND memory device comprising an n by m array of dies having n channels, each die having first and second independently accessible planes, receives read commands including instructions to access data on planes of a die. The processor determines the destination die plane of the command and sends the command to a die plane queue based on the determined destination die plane. The processor fetches commands from a head of a first die plane queue for a first plane of the destination die and a head of a second die plane queue for the second plane of the destination die, and performs reads at both the first and second planes of the destination die in parallel based on the commands.