An optimization apparatus collects cyber attack information that is information related to a cyber attack, and system information that is information related to an entire system including a device that has received the cyber attack. Based on the collected cyber attack information and system information, the optimization apparatus identifies an attack route of the cyber attack, and extracts, as dealing point candidates, devices that are on the attack route and have an effective dealing function against the cyber attack. Subsequently, the optimization apparatus selects a dealing point from the extracted dealing point candidates by using optimization logic that has been set.

In one embodiment, a device in a network receives a notification of a particular anomaly detected by a distributed learning agent in the network that executes a machine learning-based anomaly detector to analyze traffic in the network. The device computes one or more distance scores between the particular anomaly and one or more previously detected anomalies. The device also computes one or more relevance scores for the one or more previously detected anomalies. The device determines a reporting score for the particular anomaly based on the one or more distance scores and on the one or more relevance scores. The device reports the particular anomaly to a user interface based on the determined reporting score.

A method and associated system for mitigating a Distributed Denial of Service (DDoS) attack on a target device including, receiving a plurality of data packets at a mitigation device, counting a number of occurrences of each destination address signature within each of a plurality of consecutive data packet windows, classifying each data packet window of the plurality of consecutive data packet windows as a potential attack window if the number of occurrences of any one destination address signature within the data packet window exceeds a destination address signature threshold value. The method further includes, determining a total number of potential attack windows within a sliding time window and limiting the transmission of the plurality of data packets from the mitigation device if a total number of potential attack windows within the sliding time window exceeds a potential attack window threshold value.

The disclosed techniques provide systems and methods for detecting coordinated attacks on social networking databases containing personal end-user data. More specifically, various advanced persistent threat (APT) detection procedures are described that explore the commonality between specific targets of various private data accesses. In one embodiment, a threat detection tool is configured to process various private data accesses initiated by a source user account in order to identify associated query structures. The tool then applies one or more filters to the private data accesses to identify a subset of the private data accesses that have query structures indicating specific targets and processes these specific targets to determine if an access pattern exists. The access pattern can indicate, for example, a measure of commonality among two or more of the specific targets. If an access pattern exists, the threat detection tool can trigger an alarm.

Embodiments are directed to monitoring network traffic over a network using one or more network monitoring computers. A monitoring engine may be instantiated to perform actions, including: monitoring network traffic to identify client requests provided by clients and server responses provided by servers in response to the client requests; determining request metrics associated with the client requests; and determining response metrics associated with the server responses. An analysis engine may be instantiated that performs actions, including: comparing the request metrics with the response metrics; determining atypical behavior associated with the clients based on the comparison such that the atypical behavior includes an absence of adaption by the clients to changes in the server responses; and providing alerts that may identify the clients be associated with the atypical behavior.

A message protection method, user equipment, and a core network device are disclosed. The method includes: sending a request message on which no security protection is performed to the core network device, where the request message includes a first random number; receiving an abnormal response message, where the abnormal response message includes a third random number and a signature; and determining, based on the third random number, the signature, and an obtained credential, whether the abnormal response message is a valid message. According to the message protection method provided in the embodiments of the disclosure, security protection can be performed on a message transmitted before a security context is established between the user equipment and the core network device, so as to improve network communication security.

A control device is a control device in a control system in which the control device connected via a network to a server in which an appliance and a user's smartphone assigned to control the appliance are stored in association with each other receives an instruction from the user's smartphone and controls an operating condition of an appliance. The control device determines whether a signal received from a different terminal satisfies a predetermined criterion. If the control device determines that the signal satisfies the predetermined criterion, the control device cuts off all communications via the network.

A client application establishes a connection between the client application and an origin server over one or more networks. The application generates a request to establish a secure session with the origin server over the connection. The request includes information, in a header of the request, that flags traffic sent during the secure session to a network of the one or more networks as subject to one or more optimizations performed by the network. Subsequent to establishing the secure session, the application encrypts the traffic in accordance with the secure session and sends the traffic to the origin server over the connection, subject to the one or more optimizations. The infrastructure service applies the one or more optimizations to the traffic as it passes through the edge network to the origin server.

An automated vehicle parking system uses a driver's authentication device, such as a mobile phone or portable tag, to identify the driver. Vehicle sensing terminals detect when and where a vehicle has parked and send wireless notifications to the vehicle owner's authentication device. The authentication device, the vehicle sensing terminal and a cloud server interact using secure wireless communications to validate the driver's qualifications and record the parking event. Vehicle sensing terminals detect when the vehicle leaves its parking space and the parking system automatically terminates the parking session. The authentication device handles the bulk of the communication with the cloud server to reduce consumption of the vehicle sensing terminal's power supply. The sensing and portable tag devices communicate using secure tokens that are encrypted with unique individual or group keys.

A dynamic denial of service (DDoS) mitigation system comprising a BGP address family exchange connected to at least one DDoS mitigation route reflector, and at least one DDoS mitigation route reflector being an address family identifier specific route reflector, where each DDoS mitigation route reflector advertises BGP content in a first address family to the BGP family exchange. The BGP address family exchange translates the BGP content from the first address family to a destination address family and announces the translated content to a destination route reflector, and wherein the destination address family includes a flow specification diversion route.