Florida International University

Leonardo Babun

A Selcuk Uluagac

Kemal Akkaya

Hidayet Aksu

Kyle Denney

Luis C Puche Rondon

Enes Erdin

Novel hardware-based frameworks and methods for the detection and inhibition or prevention of insider threats utilizing machine learning methods and data collection done at the physical layer are provided. Analysis is done on unknown USB-powered devices, such as a keyboard or mouse, introduced to a computing environment and, through the utilization of machine learning, the behavior of the unknown device is determined before it can potentially cause harm to the computing environment.

Systems and methods for inhibiting threats to a computing environment

Systems and methods for monitoring activity within High Definition Multimedia Interface (HDMI) enabled consumer electronics control (CEC) devices and their networks and identifying unexpected and/or suspicious activity within the network are provided. CEC message packets and packet attribute analysis can be used to identify unexpected and/or suspicious CEC activity within two or more interconnected HDMI devices. Three fundamental steps can be used: a data collection step can capture CEC activity occurring within an HDMI distribution; a data processing step can correlate data into a packet analysis process to create a model later used for evaluation; and a decision process step can use the model created in the data processing step to determine if activity occurring within the HDMI distribution is expected or unexpected.

Systems and methods for monitoring activity in an HDMI network

Methods for cyber physical systems device classification are provided. A method can include receiving system and function calls and parameters and a device performance index from an unknown CPS device and a device performance index of similar class of CPS devices, calculating an autocorrelation value between different realizations of the system and function calls and parameters of the known CPS device, determining whether the autocorrelation value is greater than a threshold amount, and storing the system and function calls and parameters and the device performance characteristics of the known CPS device in the database. A method can also include calculating a correlation between system and function calls and parameters of an unknown CPS device and known CPS devices classes included in the database, as well as determining whether the maximum correlation is also greater than a threshold amount.

Method of resource-limited device and device class identification using system and function call tracing techniques, performance, and statistical analysis

Frameworks, methods, and systems for securing a smart grid are provided. A framework can include data collection, call tracing techniques, and preparing call lists to detect counterfeit or compromised devices. The call tracing techniques can include call tracing and compiling all system and function calls over a time interval. The framework can further include data processing, in which a genuine device is identified and compared to unknown devices. A first statistical correlation can be used for resource-rich systems, and a second statistical correlation can be used for resource-limited systems. Threats of information leakage, measurement poisoning and store-and-send-later can be considered.

Growing community of inventors

Miami, FL, United States of America

Leonardo Babun