Abstract: Various embodiments provide systems and methods for providing security in a ZTNA system.

Abstract: Various embodiments provide systems and methods for discovering APIs for use in relation to network application security.

Abstract: Various embodiments provide systems and methods for applying ZTNA control in a multi-level, segmented network environment.

Abstract: Various embodiments provide systems and methods for applying ZTNA control in a multi-level, segmented network environment.

Abstract: Systems, devices, and methods are discussed for providing virtualized ZTNA control across multiple networks.

Abstract: Various embodiments provide embodiments provide systems and methods for performing edge processing using selectively suspended network security processing.

Abstract: Various embodiments provide embodiments provide systems and methods for performing edge processing using selectively suspended network security processing.

Abstract: Various embodiments provide systems and methods for applying network policies to network traffic based upon a non-equal boundary search tree.

Abstract: Systems, devices, and methods are discussed for automating incident management.

Abstract: Various embodiments provide systems and methods for detecting denial of service attacks using a varying threshold.

Abstract: Systems, devices, and methods are disclosed in relation to a vector space model that may be used to characterize a category of messages. In one of many possible implementations, the frequency of words found within a piece of text is determined. These frequencies are compared against the frequencies of words within a given corpus like the Oxford English Corpus by first converting the frequencies to probabilities via the inverse cumulative distribution function assuming a normal distribution of frequencies then via taking the absolute difference in frequencies. A small difference reduces the weight of the given word whereas a large weight increases the weight of the word, leading to excellent word ranking for automated feature selection filtering without the need for a negative corpus.

Abstract: Debug engine receives a capture file over the network interface and initiate playback by executing the capture file with the processor. The capture file comprises real-time local network environment video synchronized with data captured by a local browser at a local station interacting with a local network gateway device over a local network. The capture file is played back, using a mock server including transmitting HTTP requests from the capture file at the developer station to the mock gateway server. Additionally, HTTP responses are received from the capture file at the mock gateway server, in synch with actions in the real-time local network environment video. A GUI engine renders a GUI on the developer computer from real-time GUI code generated from the capture file playback as modified by processing the HTTP responses.

Abstract: A plurality of fake vulnerabilities are exposed to network traffic alongside an active resource. Each fake vulnerability cannot harm the active resource and wherein the deceptive proxy device and the legitimate device are reachable by a common IP address. Network traffic is monitored in real-time, to detect an attack by a malicious device concerning at least one of the fake vulnerabilities of the plurality of fake vulnerabilities exposed by the deceptive proxy resource. The malicious device is trusted by the enterprise network. Responsive to the attack detection, a security action is taken with respect to the malicious device.

Abstract: A string sample is received from a file in real-time and the string sample is converted to a Tetra code and used to search a database of Tetra code samples, organized by family and then by variant. Responsive to the real-time Tetra code not matching any stored Tetra codes, (a) an internal structure of the Tetra Code is generated to expose correlations of encrypted features of the file, without any access to the file, (b) machine learning is utilized to classify the internal structure of encrypted features against training data of encrypted features, and (c) a label is predicted based on the classification. The real-time Tetra code is stored in the database associated with the new family label and/or the new variant label. Any label for the file string sample is output for potential security actions.

Abstract: Various embodiments provide systems and methods for providing security in a ZTNA system.

Abstract: Systems, devices, and methods are discussed for automatically determining a risk-based focus in determining zero trust network access policy on one or more network elements.

Abstract: Responsive to the request for a security fabric report, an upper-level node transits a request to a lower-level node for a subtree security report. If there are additional network gateways at lower hierarchical levels, the next level down repeats the process. A root level network gateway will transmit the first request, as the high level of the hierarchy, and a last leaf receives the last request, as the lowest level. An overall security fabric report is returned from the root node.

Abstract: A private network is scanned to identify devices, and profiling identified devices for vulnerabilities. A score is determined from a Common Vulnerability Scoring System (CVSS) database for each vulnerability individually that characterizes severity. A score is determined for a collection of vulnerabilities. Exponential tapering functions curb an influence of large numbers of low priority threats on the collection score. The collection threat score increases with severity of the collection of vulnerabilities.

Abstract: Example systems and methods monitor a cloud compute environment. An example method includes: determining, by an agent deployed in a cloud environment and based on a plurality of data packets transmitted over a plurality of network interfaces of the cloud environment, a set of data packets that are associated with a communication between a first container and a second container; determining, by the agent and based on the set of data packets, communication data associated with the communication; and providing, by the agent, the communication data to a data platform, wherein providing the communication data to the data platform uses less network resources than providing the set of data packets to the data platform.

Abstract: Leveraging generative artificial intelligence (‘AI’) for securing a monitored deployment, including: receiving natural language input associated with the monitored deployment, the monitored deployment monitored by a monitoring tool; and receiving, from a generative AI application, a response to the natural language input, wherein: the generative AI application accesses publicly available information as well as data sources associated with the monitoring tool; and the response is generated based at least in part on information contained in the data sources associated with the monitoring tool.