Abstract: A method for detecting false data injection attacks (FDIAs) on a condition-based predictive maintenance (CBPM) system includes: collecting sensor data from sensors monitoring components of a system maintained by the CBPM system to extract features for a cyberattack detection model and gathering historical data of the system to build a cyberattack knowledge base about the system; combining the sensor data and the historical data to train the cyberattack detection model; using a graphical Bayesian network model to capture domain knowledge and condition-symptom relationships between the sensor-monitored components and the sensors; and based on the cyberattack detection model and the Bayesian network model, detecting the FDIAs on the CBPM system.

Abstract: A method for detecting GPS spoofing attacks includes providing a trained deep learning (DL) model based on neural networks, feeding GPS signals into the trained DL model, using asymmetric Shapley values (ASVs) to calculate feature contributions, using the ASVs to assign a non-uniform distribution over an ordering of features, obtaining causal structures among the features, applying the ASVs to causal Shapley additive explanation to obtain Shapley attributions, incorporating the Shapley attributions and the causal structures, and detecting GPS spoofing attacks by running the trained DL model and using the causal structures, non-uniform distribution, feature contributions, and Shapley attributions.

Abstract: A method for detecting GPS spoofing attacks includes providing a trained deep learning (DL) model based on neural networks, feeding GPS signals into the trained DL model, using asymmetric Shapley values (ASVs) to calculate feature contributions, using the ASVs to assign a non-uniform distribution over an ordering of features, obtaining causal structures among the features, applying the ASVs to causal Shapley additive explanation to obtain Shapley attributions, incorporating the Shapley attributions and the causal structures, and detecting GPS spoofing attacks by running the trained DL model and using the causal structures, non-uniform distribution, feature contributions, and Shapley attributions.

Abstract: A method for detecting spoofing attacks on a UAV includes receiving signals from the UAV that is an edge device of a decentralized network, transmitting the signals to a fog node of the decentralized network, and detecting the spoofing attacks by detecting slow shifting patterns and running a trained attack detection model at the fog node for the signals. The decentralized network is based on an edge-fog-cloud computing paradigm.

Abstract: A method for detecting false data injection attacks (FDIAs) on a condition-based predictive maintenance (CBPM) system includes: collecting sensor data from sensors monitoring components of a system maintained by the CBPM system to extract features for a cyberattack detection model and gathering historical data of the system to build a cyberattack knowledge base about the system; combining the sensor data and the historical data to train the cyberattack detection model; using a graphical Bayesian network model to capture domain knowledge and condition-symptom relationships between the sensor-monitored components and the sensors; and based on the cyberattack detection model and the Bayesian network model, detecting the FDIAs on the CBPM system.

Abstract: Various embodiments provide a method for free space optical communication performance prediction method. The method includes: in a training stage, collecting a large number of data representing FSOC performance from external data sources and through simulation in five feature categories; dividing the collected data into training datasets and testing datasets to train a prediction model based on a deep neural network (DNN); evaluating a prediction error by a loss function and adjusting weights and biases of hidden layers of the DNN to minimize the prediction error; repeating training the prediction model until the prediction error is smaller than or equal to a pre-set threshold; in an application stage, receiving parameters entered by a user for an application scenario; retrieving and preparing real-time data from the external data sources for the application scenario; and generating near real-time FSOC performance prediction results based on the trained prediction model.

Abstract: Various embodiments provide a method for free space optical communication performance prediction method. The method includes: in a training stage, collecting a large number of data representing FSOC performance from external data sources and through simulation in five feature categories; dividing the collected data into training datasets and testing datasets to train a prediction model based on a deep neural network (DNN); evaluating a prediction error by a loss function and adjusting weights and biases of hidden layers of the DNN to minimize the prediction error; repeating training the prediction model until the prediction error is smaller than or equal to a pre-set threshold; in an application stage, receiving parameters entered by a user for an application scenario; retrieving and preparing real-time data from the external data sources for the application scenario; and generating near real-time FSOC performance prediction results based on the trained prediction model.

Abstract: Methods and systems for space situational awareness (SSA) demonstration are provided.

Abstract: Methods and systems for cyber secure data communications are provided. In some embodiments, a method for transmitting data comprises: performing a marker-based data encoding process to embed a digital watermark into each of a plurality of original data flows to be transmitted to a plurality of receivers; performing a non-orthogonal multiple access process to allocate transmission powers to the plurality of original data flows, such that the plurality of original data flows are simultaneously superposed on a carrier frequency to generate a superposed signal; performing a noise modulation process to modulate the superposed signal to generate a noise-like signal and a reference noise signal; transmitting the noise-like signal and the reference noise signal through orthogonally polarized antennas; and performing a portal-based data integrity analysis process to check whether a receiver in the plurality of receivers is compromised or manipulated.

Abstract: Method and system for detecting and tracking multiple moving targets based on wide-area motion imagery are provided. The method includes receiving a task request from a client; sending the task request to a local agent; acquiring to-be-processed images from the client, in response to the task request; calling an Hadoop API and checking a running status of a Hadoop system for an available resource in the Hadoop system; when a resource in the Hadoop system is available, pushing the to-be processed images to a back-end Hadoop distributed file system (HDFS); running tracking algorithms of multiple moving objects associated with the to-be-processed images to provide tracking results of the multiple moving objects; when the Hadoop system is completed in running the tracking algorithms, generating and sending a message to the local agent; and sending the tracking results of the multiple moving objects from the HDFS to the front-end database.

Abstract: Methods and systems for cyber secure data communications are provided. In some embodiments, a method for transmitting data comprises: performing a marker-based data encoding process to embed a digital watermark into each of a plurality of original data flows to be transmitted to a plurality of receivers; performing a non-orthogonal multiple access process to allocate transmission powers to the plurality of original data flows, such that the plurality of original data flows are simultaneously superposed on a carrier frequency to generate a superposed signal; performing a noise modulation process to modulate the superposed signal to generate a noise-like signal and a reference noise signal; transmitting the noise-like signal and the reference noise signal through orthogonally polarized antennas; and performing a portal-based data integrity analysis process to check whether a receiver in the plurality of receivers is compromised or manipulated.

Abstract: Methods and systems for space situational awareness (SSA) demonstration are provided.

Abstract: Method and system for detecting and tracking multiple moving targets based on wide-area motion imagery are provided. The method includes receiving a task request from a client; sending the task request to a local agent; acquiring to-be-processed images from the client, in response to the task request; calling an Hadoop API and checking a running status of a Hadoop system for an available resource in the Hadoop system; when a resource in the Hadoop system is available, pushing the to-be processed images to a back-end Hadoop distributed file system (HDFS); running tracking algorithms of multiple moving objects associated with the to-be-processed images to provide tracking results of the multiple moving objects; when the Hadoop system is completed in running the tracking algorithms, generating and sending a message to the local agent; and sending the tracking results of the multiple moving objects from the HDFS to the front-end database.

Abstract: In accordance with various embodiments of the disclosed subject matter, a method and a system for detecting multiple objects from real-time images are provided. The method comprises: performing, using a CPU host, an image segmentation process to divide real-time input images into a plurality of image partitions; performing, by multiple GPUs, a fast block-wise registration process, a mark setting process, a background generation process, a foreground generation process based on a Hyper-Q computation infrastructure, and a support vector machine classification process; and generating, by the CPU host, visualization classification images.

Abstract: In accordance with various embodiments of the disclosed subject matter, a method and a system for detecting multiple objects from real-time images are provided. The method comprises: performing, using a CPU host, an image segmentation process to divide real-time input images into a plurality of image partitions; performing, by multiple GPUs, a fast block-wise registration process, a mark setting process, a background generation process, a foreground generation process based on a Hyper-Q computation infrastructure, and a support vector machine classification process; and generating, by the CPU host, visualization classification images.