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.

A system and method for file type identification involving extraction of a file-print of a file, the file-print being a unique or practically-unique representation of statistical characteristics associated with the distribution of bits in the binary contents of the file, similar to a fingerprint. The file-print is then passed to a machine learning algorithm that has been trained to recognize file types from their file-prints. The machine learning algorithm returns a predicted file type and, in some cases, a probability of correctness of the prediction. The file may then be encoded using an encoding algorithm chosen based on the predicted file type.

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.

In the context of network activity by an endpoint in an enterprise network, malware detection is improved by using a combination of reputation information for a network address that is accessed by the endpoint with reputation information for an application on the endpoint that is accessing the network address. This information, when combined with a network usage history for the application, provides improved differentiation between malicious network activity and legitimate, user-initiated network activity.

An apparatus and method to distribute applications and services in and throughout a network and to secure the network includes the functionality of a switch with the ability to apply applications and services to received data according to respective subscriber profiles. Front-end processors, or Network Processor Modules (NPMs), receive and recognize data flows from subscribers, extract profile information for the respective subscribers, utilize flow scheduling techniques to forward the data to applications processors, or Flow Processor Modules (FPMs). The FPMs utilize resident applications to process data received from the NPMs. A Control Processor Module (CPM) facilitates applications processing and maintains connections to the NPMs, FPMs, local and remote storage devices, and a Management Server (MS) module that can monitor the health and maintenance of the various modules.

Disclosed are systems and methods for distributed denial of service (DDoS) protection. One or more nodes in a plurality of routes between a first node and a second node are identified. The one or more nodes can be identified at a predefined interval, or in response to one or more operational metrics exceeding a threshold. Network addresses of the identified one or more nodes are modified.

Particular embodiments described herein provide for a system that can be configured to facilitate the use of a blockchain for distributed denial of service attack mitigation, the system can include a network security provider and a validating node. The network security provider can recognize that a distributed denial of service (DDoS) attack is occurring, create a block that includes data related to the DDoS attack, and publish the block that includes the data related to the DDoS attack for addition to a blockchain. The validating node can validate the block that includes the data related to the DDoS attack and the block that includes the data related to the DDoS attack can be added to the blockchain. The block that includes the data related to the DDoS attack can be analyzed to determine how to mitigate a similar DDoS attack.

The present disclosure relates to a pre-5.sup.th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4.sup.th-Generation (4G) communication system such as Long Term Evolution. Methods and systems for mitigating Denial of Service (DOS) attacks in wireless networks, by performing admission control by verifying a User Equipment's (UE's) registration request via a Closed Access Group (CAG) cell without performing a primary authentication are provided. Embodiments herein disclose methods and system for verifying permissions of the UE to access a CAG cell based on the UE's Subscription identifier, before performing the primary authentication. The method for mitigating DOS attacks in wireless networks includes requesting a public land mobile network for accessing a non-public network (NPN) through a CAG cell, verifying the permissions of a UE to access the requested NPN through the CAG cell, and performing a primary authentication.

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.

Functionality is disclosed herein for dynamically deploying an upstream network traffic filter in a network. The upstream network filter is dynamically deployed in a location that is closer to an entry point of an attack such that attack traffic reaches the upstream network filter before reaching a network traffic filter that is configured to perform network traffic filtering for a computing resource that is under attack. The upstream network traffic filter includes rules that are based on at least a portion of the rules that are applied by the network traffic filter.