Techniques for detecting and mitigating Denial of Service (DoS) attacks in distributed networking environment are disclosed. In certain embodiments, a DoS detection and mitigation system is disclosed that automatically monitors and analyzes network traffic data in a distributed networking environment using a set of pre-defined threshold criteria. The system includes capabilities for automatically invoking various mitigation techniques that take actions on malicious traffic based on the analysis and the pre-defined threshold criteria. The system includes capabilities for automatically detecting and mitigating “outbound” DoS attacks by analyzing network traffic data originating from an entity within the network to a public network (e.g., the Internet) outside the network as well as detect and mitigate “east-west” DoS attacks by analyzing network traffic data originating from a first entity located in a first data center of the network to a second entity located in a second data center of the network.

Methods of operating a user equipment (UE) in a mobile communication network are disclosed. An authentication process start message may be transmitted from the UE to the mobile communication network, wherein the authentication process start message includes an identifier for the UE. After transmitting the authentication process start message from the UE, a request commit message may be received from the mobile communication network. Responsive to receiving the request commit message, a response commit message may be transmitted to the mobile communication network. After transmitting the response commit message, an authentication challenge message may be received corresponding to the authentication process start message. Related methods of operating network nodes are also discussed.

A system and method for generating insights on distributed denial of service (DDoS) attacks are provided. The method includes receiving a plurality of data feeds from a plurality of data sources; processing the plurality of received data feeds to generate enriched data sets; and analyzing the enriched data sets to generate insights information about a DDoS attack that have been participated in at least one DDoS attack.

A method for DoS attacks at an NF includes maintaining, at a first NF, an NF subscription database containing rules that specify maximum numbers of allowed subscriptions and corresponding rule criteria. The method further includes receiving, at the first NF and from a second NF, a subscription request for establishing a subscription. The method further includes determining, by the first NF, that the subscription request matches criteria for at least one rule in the NF subscription database and incrementing, by the first NF, at least one count of a number of subscriptions for the at least one rule. The method further includes determining, by the first NF, that the at least one count of the number of subscriptions exceeds a maximum number of allowed subscriptions for the at least one rule. The method further includes, in response to determining that the at least one count of the number of subscriptions exceeds the maximum number of allowed subscriptions for the at least one rule, preventing establishment of the subscription.

The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate.

A method of detecting cyber-attacks using network analytics in a user equipment (UE) is provided. The method includes receiving, by a network data analytics function (NWDAF), a request for sharing analytics information associated with the UE from a consumer network function (NF), requesting the analytics information associated with the UE and causing the cyber-attack to at least one 5.sup.th generation core (5GC) network entity, receiving the analytics information associated with the UE and causing the cyber-attack from the at least one 5GC network entity, comparing an expected behavior of the UE with an actual behavior based on the analytics information provided by the at least one 5GC network entity, deriving analytics associated with the cyber-attack based on the analytics information and the comparison, and sending the analytics associated with the UE to the consumer NF.

In various embodiments, a device classification service clusters devices in a network into a device type cluster based on attributes associated with the devices. The device classification service tracks changes to the device type cluster over time. The device classification service detects an attack on the device classification service by one or more of the devices based on the tracked changes to the device type cluster. The device classification service initiates a mitigation action for the detected attack on the device classification service.

A denial-of-service detection system includes a denial-of-service detection subsystem coupled to a plurality of storage systems via a network. The denial-of-service detection subsystem receives current first storage system data for each of a plurality of different storage system operating metrics from a first storage system included in the plurality of storage systems. Based on a historical storage system data for each of the plurality of different storage system operating metrics that was previously received from the plurality of storage devices, the denial-of-service subsystem detects an operating anomaly in the current first storage system data for at least one of the plurality of different storage system operating metrics, identifies a time-series similarity in a subset of respective time-series of the current first storage system data for each of the plurality of different storage system operating metrics for which the operating anomaly was detected and, in response, performs a denial-of-service remediation action.

In a communication control system having a plurality of layer 2 switches and a network controller, the network controller includes a determination unit and an instruction unit, the determination unit being configured to determine whether or not a transfer communication flow feature indicating a feature of a communication flow transferred by a layer 2 switch of the plurality of layer 2 switches is similar to an abnormal communication flow feature indicating a feature of a communication flow when an abnormality occurs; and the instruction unit being configured to: output to the layer 2 switch, when the determination unit determines that the transfer communication flow feature is similar to the abnormal communication flow feature, a first instruction to lower priority of transfer processing for the communication flow and a second instruction to duplicate the communication flow to the layer 2 switch; or output the first instruction to the layer 2 switch, and output, to a server detecting a malicious attack, identification information identifying the communication flow having the transfer communication flow feature.

A signaling attack prevention method and apparatus is provided. The signaling attack prevention method can include receiving a Diameter request message sent by a mobility management entity (MME) or a serving general packet radio service (GPRS) support node (SGSN); and determining whether the Diameter request message is received through a roaming interface. When the Diameter request message is received from the roaming interface, the signaling attack prevention method can include determining whether a characteristic parameter of the Diameter request message is valid; and if the characteristic parameter of the Diameter request message is invalid, the method can include discarding Diameter request message or returning, to the MME or the SGSN, a Diameter response message carrying an error code. In this way, a hacker can be effectively prevented from attacking an HSS or an edge node by using each attack path, and communication security is improved.

A method and system for predicting subsequent cyber-attacks in attack campaigns are provided. The method includes receiving events data related to cyber-attacks occurring in a network during a predefined time window; extracting at least one sequence from the received events data at least one attack vector; generating a sequence signature for each of the at least one extracted sequence; comparing each sequence signature to a representation of historic sequence signatures to determine at least partially matching sequence signature; and based on the matching sequence, determining at least one subsequent cyber-attack in a respective sequence.