Princeton University

Lucent Technologies Inc.

Purdue Research Foundation

Nec Laboratories America, Inc.

Other

Nec Corporation

Agere Systems Guardian Corp.

Queen's University at Kingston

Nec Usa, Inc.

Indian Statistical Institute

Niraj K Jha

Anand Raghunathan

Bharat P Dave

Ganesh Lakshminarayana

Li Shang

Arsalan Mosenia

Xiaoliang Dai

Wei Zhang

Sujit Dey

Srivaths Ravi

Hongxu Yin

Tanujay Saha

Srimat T Chakradhar

Sudipta Bhawmik

Woo Suk Lee

Vijay Raghunathan

Prateek Mittal

Loganathan Lingappan

Younghyun Kim

Lin Zhong

Indradeep Ghosh

Susmita Sur-Kolay

Kamal S Khouri

Meng Zhang

Ting-Jung Lin

Jacob Brown

Ayten Ozge Akmandor

Shayan Hassantabar

Najwa Aaraj

According to various embodiments, a system for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices is disclosed. The system includes one or more processors configured to construct an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices. The processors are further configured to generate an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator. The processors are also configured to calculate a vulnerability score and exploit risk score for each node in the aggregate attack DAG. The processors are further configured to optimize placement of defenses to reduce an adversary score of the aggregate attack DAG.

System and method for graphical reticulated attack vectors for internet of things aggregate security (gravitas)

According to various embodiments, a method for generating a compact and accurate neural network for a dataset that has initial data and is updated with new data is disclosed. The method includes performing a first training on the initial neural network architecture to create a first trained neural network architecture. The method additionally includes performing a second training on the first trained neural network architecture when the dataset is updated with new data to create a second trained neural network architecture. The second training includes growing one or more connections for the new data based on a gradient of each connection, growing one or more connections for the new data and the initial data based on a gradient of each connection, and iteratively pruning one or more connections based on a magnitude of each connection until a desired neural network architecture is achieved.

System and method for incremental learning using a grow-and-prune paradigm with neural networks

According to various embodiments, a machine-learning based system for coronavirus detection is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs and questionnaire data from a user interface. The processors are further configured to train at least one neural network based on raw physiological data and questionnaire data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one coronavirus inference model. The processors are also configured to output a coronavirus-based decision by inputting the received physiological data and questionnaire data into the generated coronavirus inference model.

System and method for testing for SARS-COV-2/COVID-19 based on wearable medical sensors and neural networks

According to various embodiments, a method for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices is disclosed. The method includes constructing an attack directed acyclic graph (DAG) from a plurality of regular expressions, where each regular expression corresponds to control-data flow for a known CPS/IoT attack. The method further includes performing a linear search on the attack DAG to determine unexploited CPS/IoT attack vectors, where a path in the attack DAG that does not represent a known CPS/IoT attack vector represents an unexploited CPS/IoT attack vector. The method also includes applying a trained machine learning module to the attack DAG to predict new CPS/IoT vulnerability exploits. The method further includes constructing a defense DAG configured to protect against the known CPS/IoT attacks, the unexploited CPS/IoT attacks, and the new CPS/IoT vulnerability exploits.

System and method for security in Internet-of-Things and cyber-physical systems based on machine learning

Devices and methods for processing detected signals at a detector using a processor are provided. The system involves (i) a data compressor that implements an algorithm for converting a set of data into a compressed set of data, (ii) a machine learning (ML) module coupled to the data compressor, the ML module transforming the compressed set of data into a vector and filtering the vector, (iii) a data encryptor coupled to the ML module that encrypts the filtered vector, and (iv) an integrity protection module coupled to the ML module, wherein the integrity protection module protects the integrity of the filtered vector.

System and method for energy efficient sensors with compression, artificial intelligence, and security

According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase.

Method and system for neural network synthesis

According to various embodiments, a stress detection and alleviation (SoDA) system for a user is disclosed. The system includes a SoDA device configured with one or more processors that receive wearable medical sensor (WMS) data from a plurality of WMSs. The processors are programmed to remove one or more artifacts from the WMS data, extract a set of features from the WMS data, remove correlated features from the extracted features to obtain a reduced set of features, classify the reduced set of features in order to determine whether the user is stressed, and generate a response based on whether the user is stressed.

Stress detection and alleviation system and method

According to various embodiments, a method for locating the user of a mobile device without accessing global position system (GPS) data is disclosed. The method includes determining the last location that the user was connected to a wireless network. The method further includes compiling publicly-available auxiliary information related to the last location. The method additionally includes classifying an activity of the user to driving, traveling on a plane, traveling on a train, or walking. The method also includes estimating the location of the user based on sensory and non-sensory data of the mobile device particular to the activity classification of the user.

System and method for tracking a mobile device user

According to some embodiments, a system for securing communications between an implantable wearable medical device (IWMD) and an external device (ED) is disclosed. The system includes a wireless radio frequency (RF) channel configured for communication between the IWMD and the ED. The system further includes a vibration-based side channel configured for verifying communication between the IWMD and the ED such that the RF channel is activated only when the IWMD detects a vibration signal generated by an ED.

Vibration-based secure side channel for medical devices

A user authentication system for an electronic device for use with a plurality of wireless wearable medical sensors (WMSs) and a wireless base station that receives a biomedical data stream (biostream) from each WMS. The system includes a BioAura engine located on a server, the server has a wireless transmitter/receiver with receive buffers that store the plurality of biostreams, the BioAura engine has a look up stage and a classifier, the classifier generates an authentication output based on the plurality of biostreams, the authentication output authenticates the user's access to the electronic device. The wireless base station has a transmitter/receiver having receive buffers that store the biomedical data from each WMS, the wireless base station has a communication engine that retrieves the biostream from each WMS and transmits the plurality of biostreams to the server.

Continuous authentication system and method based on BioAura

An implantable medical device (IMD) configured to communicate with an external device (ED). The ED supports two way RF communications and has a light source. The IMD includes a processor coupled to an optical detector, the processor is configured to verify that light is being received from the ED light source and that the ED is a trusted device, establishing a unidirectional optical channel from the ED to the IMD. An RF transceiver is coupled to the processor, the processor being configured permit two way RF communications with the ED only under a condition that the ED is verified as a trusted device. The processor may be configure to wake up periodically or aperiodically to check for the presence of light from the ED light source. The processor may be configured to detect a multi-bit message from the ED via the unidirectional optical channel. The multi-bit message may include a key.

Secure optical communication channel for implantable medical devices

A medical device monitor (MedMon), method and computer readable medium is disclosed. The MedMon is configured to operate in a system having communications between a first medical device associated with a patient and a second device. The MedMon includes a receiver configured to snoop on communications between the first medical device and second device. An anomaly detector having a set of security polices is configured to detect an anomaly by analyzing the communications between the first medical device and second device for compliance with the security policies. A response generator configured to generate a response on a condition that an anomaly is detected. The response may be a warning message configured to warn the patient. The MedMon may include a transmitter configured to transmit the response. The response may be a jamming signal configured to disrupt communications between the first medical device and second device.

Securing medical devices through wireless monitoring and anomaly detection

A field programmable gate array (FPGA) and method of reconfiguring a FPGA are disclosed. The FPGA includes a plurality of logic elements interconnected with reconfigurable switches and at least horizontal and vertical direct links A memory is coupled to the reconfigurable switches, the memory being configured to store at least two run time configurations. The reconfigurable switches are reconfigurable based on a selected run time configuration stored in the memory. The memory may be a nanoelectronic random access memory (RAM). The memory may be configured to store the at least two run time configurations for at least four logic elements. Each logic element may include a look-up-table (LUT), a flip-flop, inputs and outputs. Each logic element may include dedicated carry logic. At least four logic elements may be interconnected with diagonal direct links.

Fine-grain dynamically reconfigurable FPGA architecture

A hybrid nanotube, high-performance, dynamically reconfigurable architecture, NATURE, is provided, and a design optimization flow method and system, NanoMap. A run-time reconfigurable architecture is provided by associating a non-volatile universal memory to each logic element to enable cycle-by-cycle reconfiguration and logic folding, while remaining CMOS compatible. Through logic folding, significant logic density improvement and flexibility in performing area-delay tradeoffs are possible. NanoMap incorporates temporal logic folding during the logic mapping, temporal clustering and placement steps. NanoMap provides for automatic selection of a best folding level, and uses force-direct scheduling to balance resources across folding stages. Mapping can thereby target various optimization objectives and user constraints. A high-density, high-speed carbon nanotube RAM can be implemented as the universal memory, allowing on-chip multi-context configuration storage, enabling fine-grain temporal logic folding, and providing a significant increase in relative logic density.

Hybrid nanotube/CMOS dynamically reconfigurable architecture and system therefore

A reconfigurable computer architecture is disclosed. The reconfigurable computer architecture has a plurality of logic elements, a plurality of connection switching elements, and a plurality of volatile and/or non-volatile configuration random access memories (RAMs). Each of the configuration RAMs is electrically coupled to at least one of the plurality of logic elements or at least one of the connection switching elements.

Growing community of inventors

Princeton, NJ, United States of America

Niraj K Jha