Abstract: A system and method for protecting cloud-hosted applications against hypertext transfer protocol (HTTP) flood distributed denial-of-service (DDoS) attacks are provided. The method includes collecting telemetries from a plurality of sources deployed in at least one cloud computing platform hosting a protected cloud-hosted application; providing at least one rate-based feature and at least one rate-invariant feature based on the collected telemetries, wherein the rate-based feature and the rate-invariant feature demonstrate behavior of at least HTTP traffic directed to the protected cloud-hosted application; evaluating the at least one rate-based feature and the at least one rate-invariant feature to determine whether the behavior of the at least HTTP traffic indicates a potential HTTP flood DDoS attack; and causing execution of a mitigation action when an indication of a potential HTTP flood DDoS attack is determined.

Abstract: A method and system for protecting cloud-hosted applications against application-layer slow DDoS attacks are provided. The system include a processing circuitry; and a memory connected to the processor, the memory contains instructions that when executed by the processing circuitry, configure the system to: collect telemetries from a plurality of sources deployed in a plurality of public cloud computing platforms, wherein each of the plurality of public cloud computing platforms hosts an instance of a protected cloud-hosted application; provide a set of rate-based and rate-invariant features based on the collected telemetries; evaluate each feature in the set of rate-based and rate-invariant features to determine whether a behavior of each feature and a behavior of the set of rate-based and rate-invariant features indicate a potential application-layer slow DDoS attack; and cause execution of a mitigation action, when an indication of a potential application-layer slow DDoS attack is determined.

Abstract: A method and system for protecting cloud-hosted applications against application-layer slow distributed denial-of-service (DDoS) attacks. The comprising collecting telemetries from a plurality of sources deployed in at least one cloud computing platform hosting a protected cloud-hosted application; providing a set of rate-based and rate-invariant features based on the collected telemetries; evaluating each feature in the set of rate-based and rate-invariant features to determine whether a behavior of each feature and a behavior of the set of rate-based and rate-invariant features indicate a potential application-layer slow DDoS attack; and causing execution of a mitigation action, when an indication of a potential application-layer slow DDoS attack is determined.

Abstract: A system and method for real-time tuning of inference systems based on quality of incoming data. The method comprises: periodically receiving traffic data collected by a plurality of collectors deployed in a network; determining at least a normalized variance of a current sample of the received traffic data; estimating, based in part on the normalized variance, a standard deviation of the received traffic data and a fading coefficient of a baseline filter; determining a current baseline value based on a previous baseline value, the fading coefficient, and the current sample of the traffic data; and dynamically setting at least one membership function of the inference system based in part on the current baseline value and the standard deviation.

Abstract: A system and method for protecting cloud-hosted applications against hypertext transfer protocol (HTTP) flood distributed denial-of-service (DDoS) attacks are provided. The method includes collecting telemetries from a plurality of sources deployed in at least one cloud computing platform hosting a protected cloud-hosted application; providing at least one rate-based feature and at least one rate-invariant feature based on the collected telemetries, wherein the rate-based feature and the rate-invariant feature demonstrate behavior of at least HTTP traffic directed to the protected cloud-hosted application; evaluating the at least one rate-based feature and the at least one rate-invariant feature to determine whether the behavior of the at least HTTP traffic indicates a potential HTTP flood DDoS attack; and causing execution of a mitigation action when an indication of a potential HTTP flood DDoS attack is determined.

Abstract: A method and system for protecting cloud-hosted applications against application-layer slow distributed denial-of-service (DDoS) attacks. The comprising collecting telemetries from a plurality of sources deployed in at least one cloud computing platform hosting a protected cloud-hosted application; providing a set of rate-based and rate-invariant features based on the collected telemetries; evaluating each feature in the set of rate-based and rate-invariant features to determine whether a behavior of each feature and a behavior of the set of rate-based and rate-invariant features indicate a potential application-layer slow DDoS attack; and causing execution of a mitigation action, when an indication of a potential application-layer slow DDoS attack is determined.

Abstract: A system and method for real-time tuning of inference systems based on quality of incoming data. The method comprises: periodically receiving traffic data collected by a plurality of collectors deployed in a network; determining at least a normalized variance of a current sample of the received traffic data; estimating, based in part on the normalized variance, a standard deviation of the received traffic data and a fading coefficient of a baseline filter; determining a current baseline value based on a previous baseline value, the fading coefficient, and the current sample of the traffic data; and dynamically setting at least one membership function of the inference system based in part on the current baseline value and the standard deviation.