Ciena Corporation

James P'ford't Carnes, Iii

David Jordan Krauss

Petar Djukic

Balaji Subramaniam

Kyle Gosselin-Harris

Burak Kantarci

Murat Simsek

William Kaufmann

Jinxin Liu

Zhiyan Chen

Mehran Bagheri

Yu Shen

Systems and methods for monitoring a network slice are provided. A method, according to one implementation, include extracting information from network traffic received from one or more User Plane Function (UPF) components of a network slice; examining the extracted information using Machine Learning (ML), and, in response to detecting of one or more malicious threats based on the examined extracted information by the ML, causing one or more actions to isolate the network traffic to protect at least the network slice from the one or more malicious threats.

Detecting malicious threats in a 5G network slice

Systems and methods for detecting intrusions, attacks, and sub-attacks launched against a network under observations are provided. A method, according to one implementation, includes obtaining network traffic information regarding data traffic in a network under observation and obtaining system log information regarding operations of the network under observation. The method further includes the step of inserting the network traffic information and system log information into one or more analysis procedures, where each analysis procedure is configured to detect a respective sub-attack of a multi-stage attack to which the network under observation is susceptible. Also, the method includes the step of combining the outputs of the one or more analysis procedures to detect whether one or more sub-attacks have been launched against the network under observation. In response to detecting that one or more sub-attacks have been launched, the methods include the step of determining the type of the one or more sub-attacks.

Machine learning detection of network attacks using traffic and log information

Systems, methods, and computer-readable media are provided for protecting a network from network discovery by an unknown or unauthenticated device. A network element, according to one implementation, is arranged in a communication network and includes an interface device configured to enable communication with an unknown network device. The network element also includes a memory system configured to store first and second sets of network information. The first set of network information includes fake information about the network and is configured to deceive the unknown network device before a procedure is performed for analyzing the authentication of the unknown network device. The second set of network information includes real information about the network. In some cases, the first set of network information may be provided to the unknown network device via the interface device before performing the procedure for analyzing the authentication of the unknown network device.

Preventing network discovery by untrusted devices

Systems and methods include receiving a request for a path in a network including a plurality of network elements interconnected to one another via links, wherein the request includes values for a plurality of criteria, wherein the plurality of criteria include one or more of trust, privacy, and secrecy; utilizing a multi-criteria path selection process to determine the path through the plurality of network elements over the links based on the plurality of criteria and the associated values; and providing a display of the determined path in a network map. The trust quantifies trustworthiness of each link in the network and the values of trust are any of a rating and a selection for inclusion or exclusion, the privacy quantifies a number of the links the network path is routed over for network obfuscation, and the secrecy quantifies a level of encryption utilized on the links.

Adjustable multi-criteria network path computation and visualizations thereof

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Baltimore, MD, United States of America

James P'ford't Carnes, Iii