Blackberry Corporation

Ciena Corporation

Murat Simsek

Burak Kantarci

Jinxin Liu

Andrew James Malton

Andrew Eric Walenstein

Melike Erol Kantarci

Petar Djukic

James P'ford't Carnes, Iii

Zhiyan Chen

Mehran Bagheri

Yu Shen

A risk-aware access control system and related methods are provided. In accordance with one aspect of the present disclosure, there is a provided a method of risk-aware access control, comprising: detecting a request to perform an action with respect to two factors, the factors being of a factor type selecting people, devices, documents, and location, wherein the factors are of a different factor type; determining a coupling associated with the requested action based on the factors of the requested action; determining a risk level associated with the coupling; denying the requested action in response to a determination that the risk level does not match a security policy; and allowing the requested action in response to a determination that the risk level matches the security policy.

Risk-aware access control system and related methods

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

Growing community of inventors

Ottawa, Canada

Murat Simsek