Methods and systems for mitigating a spoofing-based attack include calculating a travel distance between a source Internet Protocol (IP) address and a target IP address from a received packet based on time-to-live information from the received packet. An expected travel distance between the source IP address and the target IP address is estimated based on a sparse set of known source/target distances. It is determined that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security action is performed responsive to the determination that the received packet has a spoofed source IP address.

A monitoring service obtains request data specifying entries corresponding to requests received by a Domain Name System service to obtain an Internet Protocol address for a resource and to requests received by a web service to access the resource. The monitoring service uses that request data to generate a request frequency value corresponding to the received requests and compares this value to a baseline request frequency value. If the request frequency value exceeds the baseline request frequency value by a maximum threshold value, the monitoring service performs an operation to redirect network traffic originally directed towards the web service.

A threat detection system for a mobile communication system, and a global device and a local device thereof are provided. The threat detection system is used for detecting and defensing low and slow distributed denial-of-service (LSDDoS) attacks. The global device is located in a core network of the mobile communication system, and is used for training a tensor neural network (TNN) model to build a threat classifier. The threat classifier is used for the local device to identify a plurality of threat types. The local device inputs the to-be-identified data into the threat classifier to generate a classification result corresponding to one of the threat types.

In one embodiment, a device in a network receives traffic data regarding a plurality of observed traffic flows. The device maps one or more characteristics of the observed traffic flows from the traffic data to traffic characteristics associated with a targeted deployment environment. The device generates synthetic traffic data based on the mapped traffic characteristics associated with the targeted deployment environment. The device trains a machine learning-based traffic classifier using the synthetic traffic data.

A computer-implemented method, computer program product and computer system include a processor(s) receiving request from a first client for an attribute of a first service node to utilize to access the service provided. The processor(s) provides the attribute of the first service node to the first client. The processor(s) accepts an access to the service by the first client, based on the first client utilizing the attribute to connect to the first service node. The processor(s) identifies attributes of one or more clients accessing the service via the first service node, including the first client. The processor(s) experiences an event indicating a need to change security protecting access to the service. The processor(s) redistributes the one or more clients to at least two additional service nodes.

Techniques and mechanisms are disclosed for configuring actions to be performed by a network security application in response to the detection of potential security incidents, and for causing a network security application to report on the performance of those actions. For example, users may use such a network security application to configure one or more modular alerts. As used herein, a modular alert generally represents a component of a network security application which enables users to specify security modular alert actions to be performed in response to the detection of defined triggering conditions, and which further enables tracking information related to the performance of modular alert actions and reporting on the performance of those actions.

The disclosed computer-implemented method includes applying transport protocol heuristics to selective acknowledgement (SACK) messages received at a network adapter from a network node. The transport protocol heuristics identify threshold values for operational functions that are performed when processing the SACK messages. The method further includes determining, by applying the transport protocol heuristics to the SACK messages received from the network node, that the threshold values for the transport protocol heuristics have been reached. In response to determining that the threshold values have been reached, the method includes identifying the network node as a security threat and taking remedial actions to mitigate the security threat. Various other methods, systems, and computer-readable media are also disclosed.

Endpoint security systems and methods include a distance estimation module configured to calculate a travel distance between a source Internet Protocol (IP) address and an IP address for a target network endpoint system from a received packet received by the target network endpoint system based on time-to-live (TTL) information from the received packet. A machine learning model is configured to estimate an expected travel distance between the source IP address and the target network endpoint system IP address based on a sparse set of known source/target distances. A spoof detection module is configured to determine that the received packet has a spoofed source IP address based on a comparison between the calculated travel distance and the expected travel distance. A security module is configured to perform a security action at the target network endpoint system responsive to the determination that the received packet has a spoofed source IP address.

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.

A cyber security threat detection system for one or more endpoints within a computing environment is disclosed. The system includes one or more collector engines. Each of the collector engines includes a service and an agent operating on a corresponding system endpoint of the system endpoints. The service is configured to take a first snapshot of the corresponding system endpoint. The first snapshot includes event activity information associated with the system endpoint. The agent is configured to take a second snapshot of the corresponding system endpoint. The second snapshot includes behavioral activity information associated with the corresponding system endpoint. The system further includes an aggregator engine configured to aggregate the first snapshot and the second snapshot from each of the system endpoints into an aggregated snapshot. The system further includes one or more analytics engines configured to: generate and store baseline profiles associated with the system endpoints based on a previously received aggregated snapshot, receive the aggregated snapshot from the aggregator engine, determine deviation values for each of the system endpoints based on the received aggregated snapshot and the stored baseline profiles, and generate, for each of the system endpoints, a cumulative risk value based on the deviation values. The system further includes one or more alerting engines configured to determine whether to issue one or more alerts indicating one or more security threats have occurred for each of the endpoints in response to the cumulative risk value.