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21	Deep Learning Based Models for Cognitive Autonomy and Cybersecurity Intelligence in Autonomous Systems Ganapathy Mani (8840606) 21 June 2022 (has links) Cognitive autonomy of an autonomous system depends on its cyber module's ability to comprehend the actions and intent of the applications and services running on that system. The autonomous system should be able to accomplish this without or with limited human intervention. These mission-critical autonomous systems are often deployed in unpredictable and dynamic environments and are vulnerable to evasive cyberattacks. In particular, some of these cyberattacks are Advanced Persistent Threats where an attacker conducts reconnaissance for a long period time to ascertain system features, learn system defenses, and adapt to successfully execute the attack while evading detection. Thus an autonomous system's cognitive autonomy and cybersecurity intelligence depend on its capability to learn, classify applications (good and bad), predict the attacker's next steps, and remain operational to carryout the mission-critical tasks even under cyberattacks. In this dissertation, we propose novel learning and prediction models for enhancing cognitive autonomy and cybersecurity in autonomous systems. We develop (1) a model using deep learning along with a model selection framework that can classify benign and malicious operating contexts of a system based on performance counters, (2) a deep learning based natural language processing model that uses instruction sequences extracted from the memory to learn and profile the behavior of evasive malware, (3) a scalable deep learning based object detection model with data pre-processing assisted by fuzzy-based clustering, (4) fundamental guiding principles for cognitive autonomy using Artificial Intelligence (AI), (5) a model for privacy-preserving autonomous data analytics, and finally (6) a model for backup and replication based on combinatorial balanced incomplete block design in order to provide continuous availability in mission-critical systems. This research provides effective and computationally efficient deep learning based solutions for detecting evasive cyberattacks and increasing autonomy of a system from application-level to hardware-level. <br> Private policing and security services Knowledge representation and reasoning Pattern recognition System and network security Data mining and knowledge discovery Artificial Intelligence Cybersecurity Deep Learning Machine Learning Cryptomining Cryptojacking Natural Language Processing Malware Combinatorics Autonomous Systems Cognitive Autonomy Applied Computer Science Computer System Security Private Policing and Security Services Pattern Recognition and Data Mining
22	Complex Vehicle Modeling: A Data Driven Approach Alexander Christopher Schoen (8068376) 31 January 2022 (has links) <div> This thesis proposes an artificial neural network (NN) model to predict fuel consumption in heavy vehicles. The model uses predictors derived from vehicle speed, mass, and road grade. These variables are readily available from telematics devices that are becoming an integral part of connected vehicles. The model predictors are aggregated over a fixed distance traveled (i.e., window) instead of fixed time interval. It was found that 1km windows is most appropriate for the vocations studied in this thesis. Two vocations were studied, refuse and delivery trucks.</div><div><br></div><div> The proposed NN model was compared to two traditional models. The first is a parametric model similar to one found in the literature. The second is a linear regression model that uses the same features developed for the NN model.</div><div><br></div><div> The confidence level of the models using these three methods were calculated in order to evaluate the models variances. It was found that the NN models produce lower point-wise error. However, the stability of the models are not as high as regression models. In order to improve the variance of the NN models, an ensemble based on the average of 5-fold models was created. </div><div><br></div><div> Finally, the confidence level of each model is analyzed in order to understand how much error is expected from each model. The mean training error was used to correct the ensemble predictions for five K-Fold models. The ensemble K-fold model predictions are more reliable than the single NN and has lower confidence interval than both the parametric and regression models.</div> Computer Engineering Genetic Engineering Environmental Engineering Modelling Pattern Recognition and Data Mining Simulation and Modelling Neural Network Prediction fuel consumption improvements Ensemble Averaging Complex system modeling Refuse Truck Delivery Truck Vehicle Routing Vehicle Routing Problem SAE J1321 synthetic data generation power take-off Aerodynamic Speed Characteristic Acceleration Artificial neural network feature importances predictor importance Influence of Weights Point-wise Prediction Error Machine Learning
23	3D OBJECT DETECTION USING VIRTUAL ENVIRONMENT ASSISTED DEEP NETWORK TRAINING Ashley S Dale (8771429) 07 January 2021 (has links) <div> <div> <div> <p>An RGBZ synthetic dataset consisting of five object classes in a variety of virtual environments and orientations was combined with a small sample of real-world image data and used to train the Mask R-CNN (MR-CNN) architecture in a variety of configurations. When the MR-CNN architecture was initialized with MS COCO weights and the heads were trained with a mix of synthetic data and real world data, F1 scores improved in four of the five classes: The average maximum F1-score of all classes and all epochs for the networks trained with synthetic data is F1∗ = 0.91, compared to F1 = 0.89 for the networks trained exclusively with real data, and the standard deviation of the maximum mean F1-score for synthetically trained networks is σ∗ <sub>F1 </sub>= 0.015, compared to σF 1 = 0.020 for the networks trained exclusively with real data. Various backgrounds in synthetic data were shown to have negligible impact on F1 scores, opening the door to abstract backgrounds and minimizing the need for intensive synthetic data fabrication. When the MR-CNN architecture was initialized with MS COCO weights and depth data was included in the training data, the net- work was shown to rely heavily on the initial convolutional input to feed features into the network, the image depth channel was shown to influence mask generation, and the image color channels were shown to influence object classification. A set of latent variables for a subset of the synthetic datatset was generated with a Variational Autoencoder then analyzed using Principle Component Analysis and Uniform Manifold Projection and Approximation (UMAP). The UMAP analysis showed no meaningful distinction between real-world and synthetic data, and a small bias towards clustering based on image background. </p></div></div></div> Computer Engineering Software Engineering Applied Computer Science Information Systems Signal Processing Computer Graphics Computer Vision Image Processing Pattern Recognition and Data Mining Simulation and Modelling Virtual Reality and Related Simulation Applied Discrete Mathematics Coding and Information Theory Conceptual Modelling Information Engineering and Theory Machine Learning Mask R-CNN Artificial Intelligence Image Processing 3D Image Multispectral Data Signal Processing CNN DNN Object Detection Threat Detection Virtual Environments Synthetic Dataset F1 score image segmentation methods Image Segmentation RGBD RGBD video RGBZ Algorithms MS COCO Microsoft Common Objects in Context Transfer Learning

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