Aspects of the disclosure relate to an automated monitoring of proximate devices. A computing platform may cause a reporting device to detect a target device in a local network, retrieve network data associated with the target device, and send, to an intermediate server, the network data. The computing platform may send, to the intermediate server, a query. The intermediate server may send the network data in response to the query. Based on the network data, the computing platform may determine an amount of time that has elapsed since network activity was previously detected for the target device, and based on a determination that the amount of time exceeds a predetermined time threshold, the computing platform may generate an alert notification indicating that the target device may need to be traced. Subsequently, the alert notification may be sent to the reporting device.

There are provided systems and methods for identifying patterns in computing attacks through an automated traffic variance finder. A service provider, such as an electronic transaction processor for digital transactions, may determine network traffic logs caused or generated by malicious web traffic and network communications, such as during a computing attack by a bad actor. The service provider may generate a log signature for the network traffic log based on a variance or uniqueness of the network traffic logs IP address from other network traffic logs for each field in the network traffic log over a time period, and a spread in the commonality of the network traffic log with other network traffic logs. An aggregate score for each field may be determined based on the variance and the spread. Once determined, the log signature may be used to identify other network traffic logs through a search function.

Technologies for providing shared memory for accelerator sleds includes an accelerator sled to receive, with a memory controller, a memory access request from an accelerator device to access a region of memory. The request is to identify the region of memory with a logical address. Additionally, the accelerator sled is to determine from a map of logical addresses and associated physical address, the physical address associated with the region of memory. In addition, the accelerator sled is to route the memory access request to a memory device associated with the determined physical address.

A method includes determining a corresponding level of a security model associated with each device of a plurality of devices connected to a network, each level of the security model having a corresponding tag; applying, to each of the plurality of devices, the corresponding tag based on the corresponding level of the security model with which each of the plurality of devices are associated; receiving, over a network connection, network traffic from at least one of the plurality of devices and the corresponding tag; analyzing the corresponding tag associated with the network traffic; determining a destination for the network traffic; applying one or more security measures to the network traffic based on the corresponding tag for the at least one device and a corresponding tag of the destination for the network traffic; and sending the network traffic to the destination with the corresponding tag of the destination.

A method for providing online security may include: (1) receiving, by a validation computer program executed by a trusted entity backend for a trusted entity, a call from a web browser executed on a customer electronic device browsing a webpage for an online entity, the call comprising an online entity identifier for the online entity and a session identifier, wherein the webpage for the online entity may include a hidden <iframe> comprising code that causes the web browser to execute the call; (2) confirming, by the validation computer program, that a cookie for the trusted entity may be stored on the customer electronic device; and (3) returning, by the validation computer program, a first value indicating that the customer electronic device is known to the trusted entity or a second value indicating that the customer electronic device is not known to the trusted entity based on the confirmation.

A protocol state fuzzing method for security of a control plane of a distributed software-defined network is provided. The protocol state fuzzing method includes receiving input alphabets being abstract symbols of a protocol message in an ambusher of a distributed network operating system (NOS), converting the input alphabets into the protocol message, and sending the protocol message to a cluster, monitoring, by the cluster, intercommunication between instances in the distributed NOS, and selecting a set of sequences executable in the cluster and searching a cluster log for an output by executing the sequence to generate an attack result.

According to an example, an autonomous normal and novel behavior sharing apparatus may receive one or more novel behavior baseline models and one or more normal behavior baseline models from a first entity for sharing with a second entity and a subset of other entities; share the received models with the second entity and a subset of other entities; receive one or more novel behavior baseline models and one or more normal behavior baseline models from other entities for sharing with the first entity and a subset of other entities; share the received models with the first entity and subset of other entities; receive effectiveness factor of the shared models from the entities that received these models; score the models based on effectiveness factor received from a plurality of entities; prioritize sharing of the models based on their score.

Security can be improved in a business application or system, such as a mission-critical application, by automatically analyzing and detecting anomalies for mission-critical applications. This detection may be based on a dynamic analysis of business process logs and audit trails that includes User and Entity Behavior Analysis (“UEBA”).

A processor may identify one or more predicted microservice chains for each of one or more user profiles. The one or more predicted microservice chains may be selected based on historical information. The one or more user profiles may each be associated with a respective user of a user device. The processor may analyze user specific information. The user specific information may be associated with the user device. The processor may determine, based on the user specific information, if the user device causes network intrusion. The processor may perform, based on the determination, an action for the user device.

Managing security access in real-time to a computer system using control lists includes detecting a security event at a computer system. The security event is analyzed including an analysis of a historical corpus having historical data of security events. An access control list is generated based on the security event. A determination is made when the security event includes abnormal behavior based on the analysis of the security event and the historical corpus. The security event is published to a monitoring system for controlling access to the computer system, in response to the security event.