Graph Convolutional Networks (GCNs) have fueled a surge of interest due to their superior performance on graph learning tasks, but are also shown vulnerability to adversarial attacks. In this paper, an effective graph structural attack is investigated to disrupt graph spectral filters in the Fourier domain. We define the spectral distance based on the eigenvalues of graph Laplacian to measure the disruption of spectral filters. We then generate edge perturbations by simultaneously maximizing a task-specific attack objective and the proposed spectral distance. The experiments demonstrate remarkable effectiveness of the proposed attack in the white-box setting at both training and test time. Our qualitative analysis shows the connection between the attack behavior and the imposed changes on the spectral distribution, which provides empirical evidence that maximizing spectral distance is an effective manner to change the structural property of graphs in the spatial domain and perturb the frequency components in the Fourier domain.

Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. Decisions about how to respond involve coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations. Currently, playbooks are used to automate portions of a response process, but often leave complex decision-making to a human analyst. In this work, we present a deep reinforcement learning approach to autonomous response and recovery in large industrial control networks. We propose an attention-based neural architecture that is flexible to the size of the network under protection. To train and evaluate the autonomous defender agent, we present an industrial control network simulation environment suitable for reinforcement learning. Experiments show that the learned agent can effectively mitigate advanced attacks that progress with few observable signals over several months before execution. The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network. The learned policy is also more robust to changes in attacker behavior than playbook approaches.

Closed-loop verification of cyber-physical systems with neural network controllers offers strong safety guarantees under certain assumptions. It is, however, difficult to determine whether these guarantees apply at run time because verification assumptions may be violated. To predict safety violations in a verified system, we propose a three-step framework for monitoring the confidence in verification assumptions. First, we represent the sufficient condition for verified safety with a propositional logical formula over assumptions. Second, we build calibrated confidence monitors that evaluate the probability that each assumption holds. Third, we obtain the confidence in the verification guarantees by composing the assumption monitors using a composition function suitable for the logical formula. Our framework provides theoretical bounds on the calibration and conservatism of compositional monitors. In two case studies, we demonstrate that the composed monitors improve over their constituents and successfully predict safety violations.

Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear regressions models are each built from a highly skewed non-normal dataset with multicollinearity and compared. Several techniques are necessary, such as data transformation, to achieve a good multiple linear regression model with this dataset; the most useful techniques are discussed. With these techniques, the best multiple linear regression model achieved an R^2 = 81.23% and exhibited no multicollinearity effect for the dataset used in this study. Tree-based models perform better on this dataset, as they are non-parametric, capable of handling complex relationships among variables and not affected by multicollinearity. We show that bagging, in the use of Random Forests, reduces overfitting. Our best tree-based model achieved accuracy of R^2 = 97.73%. This study explains why tree-based regressions promise as a machine learning model for non-normally distributed, multicollinear data.

The Internet of Vehicles (IoV), whereby interconnected vehicles communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits but also poses new cyber-physical threats. Data on vehicular attackers can be realistically gathered through cyber threat intelligence using systems like honeypots. Admittedly, configuring honeypots introduces a trade-off between the level of honeypot-attacker interactions and any incurred overheads and costs for implementing and monitoring these honeypots. We argue that effective deception can be achieved through strategically configuring the honeypots to represent components of the IoV and engage attackers to collect cyber threat intelligence. In this paper, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar builds upon a repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) data within the National Vulnerability Database (NVD) to compute optimal honeypot configuration strategies. By taking a game-theoretic approach, we model the adversarial interaction as a repeated imperfect-information zero-sum game in which the IoV network administrator chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability of the IoV to exploit under uncertainty. Our investigation is substantiated by examining two different versions of the game, with and without the re-configuration cost to empower the network administrator to determine optimal honeypot configurations. We evaluate HoneyCar in a realistic use case to support decision makers with determining optimal honeypot configuration strategies for strategic deployment in IoV.

Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most societal operations. However, due to the requirements of reliability, accountability, and explainability, it is risky to directly apply AI-based methods to power systems because society cannot afford cascading failures and large-scale blackouts, which easily cost billions of dollars. To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model. The goal is to provide a high level of confidence to energy utility users. For illustration purposes, the paper uses power system event forecasting (PEF) as an example, which carefully analyzes synchrophasor patterns measured by the Phasor Measurement Units (PMUs). Such a physical understanding leads to a data-driven framework that reduces the dimensionality with physics and forecasts the event with high credibility. Specifically, for dimensionality reduction, machine learning arranges physical information from different dimensions, resulting inefficient information extraction. For event forecasting, the supervised learning model fuses the results of different models to increase the confidence. Finally, comprehensive experiments demonstrate the high accuracy, efficiency, and reliability as compared to other state-of-the-art machine learning methods.

Computer simulations that scientists use to understand the evolution of the Earth's climate offer a wealth of information to public officials and corporations planning for the future. However, climate models -- no matter how complex or computationally intensive -- do contain some degree of uncertainty. Addressing this uncertainty is proving increasingly important as decision makers are asking more complex questions and looking to smaller scales. To improve climate simulations, scientists are looking to the potential of artificial intelligence (AI). AI has offered profound insights in fields from materials science to manufacturing, and climate researchers are excited to explore how AI can be used to revolutionize how the Earth system, and especially its water cycle, can be simulated in order to dramatically improve our understanding and representation of the real world.

BE PART OF BUILDING THE FUTURE. What do NASA and emerging space companies have in common with COVID vaccine R&D teams or with Roblox and the Metaverse? The answer is data, -- all fast moving, fast growing industries rely on data for a competitive edge in their industries. And the most advanced companies are realizing the full data advantage by partnering with Pure Storage. Pure's vision is to redefine the storage experience and empower innovators by simplifying how people consume and interact with data.

Lawrence currently volunteers as the COVID data scientist on Ridgefield's COVID-19 Task Force, providing daily analysis of the latest COVID-19 data to help town officials make science-based policy decisions, and provides periodic analysis of vaccination rates to the Office of the Governor of Connecticut. Lawrence's work has evolved from nuclear science to computer science to machine learning and, most recently, to quantitative finance. He joined IBM Research in Yorktown Heights, New York, in 1987, where he held a number of management positions, most recently as Distinguished Research Staff Member and Senior Manager, Machine Learning & Decision Analytics. From 2016 to 2019, he was president of PCIX, Inc., a New York City venture capital-funded startup that used machine learning to extract quantitative insight on the relationship between private-equity transactions and the performance of public markets. Lawrence received a Bachelor of Science in Chemical Engineering from Stanford University and a doctorate in Nuclear Engineering from the University of Illinois.

Facebook is shutting down its facial recognition program and deleting more than 1 billion users' faceprints, a company official said Tuesday. The move means more than one-third of Facebook's daily active users – about 640 million people – who have opted into the social network's facial recognition option no longer will be automatically recognized in photos and videos, said Jerome Pesenti, vice president of artificial intelligence at Meta, the newlynamed parent company of Facebook, in a blog post. Also affected: Facebook's automatic alt text system, which uses facial recognition and artificial intelligence to give those who are blind or visually impaired descriptions of images that let them know when they or a friend are in an image. Facebook is taking this action, Pesenti said, because "the many specific instances where facial recognition can be helpful need to be weighed against growing concerns about the use of this technology as a whole." In addition to societal concerns about how facial recognition may be used, "regulators are still in the process of providing a clear set of rules governing its use," he said.