Ground segmentation from LiDAR point cloud data plays a critical role in both civil engineering and autonomous vehicle systems. However, real-world LiDAR data often suffers from geometric distortions, occlusions, and dense clutter, which limit the reliability and accuracy of ground segmentation. To overcome these challenges, we introduce KPU-Net. This deep neural network architecture employs: 1) T-Net module, which handles geometric distortions by aligning point clouds into a canonical pose, 2) KPConv-augmented U-Net encoder-decoder, which handles occlusions, clutter, and irregular terrain by capturing fine-grained, hierarchical features through learned kernel point convolutions over local neighborhoods. In addition to above, KPU-Net offers following advantages: 3) KPU-Net features high speed processing (approximately 231K points per second making it well-suited for scalable deployment in mapping and perception systems), while 4) preserving the original point cloud density (i.e no loss in point cloud data, avoiding sparsification that can compromise precision in various applications). The framework was trained and tested on a benchmark dataset and diverse point cloud data collected by the Timmons group, covering urban, vegetation, and complex terrain environments. The presented KPU-Net was evaluated against five widely used LIDAR data segmentation methods: Random Forest, PointNet, GndNet, RandLA-Net, and KPConv. The proposed KPU-Net demonstrated better performance on mean Intersection over Union (mIoU, up to 33%), mean accuracy (mAcc, up to 25%), and overall accuracy (OA, up to 19%), over the five compared state-of-the-art methods.

The Internet of Vehicles (IoV) has transformed intelligent transportation systems through vehicle-to-everything (V2X) communication, improving road safety and traffic efficiency. However, the dynamic nature of vehicular networks, with high mobility and shared wireless resources, makes them vulnerable to attacks like Denial of Service (DoS). Anomaly detection (AD) has proven effective in detecting such threats. Yet, V2X communication occurs in diverse environments with varying network coverage and vehicle speeds, leading to domain shifts and variations in feature distributions that can hinder the generalization performance of traditional anomaly detection models. To address these challenges, this paper presents V2XFormer, an unsupervised anomaly detection system based on transformer neural networks, designed to identify anomalies in V2X communication. Additionally, we introduce TV2XFormer, which integrates transfer learning to enhance adaptability across diverse network conditions and environmental variations in V2X communication. We assess the performance of the proposed approaches using the VDoS-LRS V2X dataset, employing precision, recall, and$\mathbf{F 1}$score metrics. A comparison is made with five state-of-the-art unsupervised AD algorithms. Experimental results demonstrate that both V2XFormer and TV2XFormer outperform the competing algorithms, achieving the highest$\mathbf{F 1}$scores (1.0). Furthermore, TV2XFormer exhibits notable robustness and generalizability to dynamic vehicular environments.

Machine learning and deep neural networks (DNNs) have been proposed as a tool to identify anomalies in computer network communications. However, due the obfuscatednature of off-the-shelf machine learning models, their output often does not provide enough information to isolate the source of the anomaly to take corrective measures. In this article, we introduce the network transformer (NeT), a DNN model for anomaly detection that incorporates the graph structure of the communication network in order to improve interpretability. The presented approach has the following advantages: first, enhanced interpretability by incorporating the graph structure of computer networks; second, provides a hierarchical set of features that enables analysis at different levels of granularity; second, self-supervised training that does not require labeled data. The NeT model was evaluated on a set of anomalous scenarios executed in a real industrial control system. The presented approach successfully identified the anomalies, the devices affected, and the specific connections causing the anomalies, providing a data-driven hierarchical approach to analyze the behavior of a cyber network.

The advancements in Cyber-Physical Systems (CPSs) have also increased their vulnerability to various cyber-attacks. Therefore, it is crucial to develop strong cybersecurity mechanisms, shielding these critical systems from potential cyber intrusions. Among many AI technologies, Generative AI (GenAI) has gained significant attention in the last couple of years. This is due to its distinctive capability to autonomously generate original and diverse content across different domains, offering potential for novel advancements in several applications. Given the massive success of GenAI, it is essential to explore its role in ensuring the cybersecurity of CPSs. Therefore, in this paper, we present: 1) the evolution and current state of GenAI, 2) benefits of GenAI on the cybersecurity of CPS, 3) threats of GenAI on the cybersecurity of CPS, 4) defense strategies against threats and 5) future research opportunities. We hope this systematic survey will help the community prioritize research efforts to address pressing issues in cybersecurity of CPSs.

With the advancements of modern vehicle infrastructures, vehicles are increasingly relying on the signals received from a vast number of sensors and electronic components. Wireless technologies enable communication between vehicles and infrastructure, but it also increase the vulnerability surface. Malicious actors can remotely disrupt the vehicle's normal behavior, causing vehicle damage or worse, putting human lives in danger. To address these challenges, this paper proposes a transformer neural network-based intrusion detection system (CAN-Former IDS) that predicts anomalous behavior within the CAN protocol communication. Previous work typically addresses the prediction over the sequence of the CAN IDs. In this paper, we will simultaneously analyze both the sequence of IDs and the message payload values. The advantages of our approach are: 1) fully self-supervised training, which does not require labeled data, 2) self learning interactions between input tokens without relying on hand-crafted features. The transformer neural network is trained to predict the next communication sequence and anomalous communication is identified by comparing the real sequence to the predicted expected sequence. We evaluated our approach using a publicly available data set known as survival analysis data set, containing CAN communication from three different cars.

Modern vehicles have multiple electronic control units (ECUs) that are connected as part of a complex cyber-physical system (CPS). The controller area network (CAN) is a well-known communication protocol that connects these ECUs because of its reliability and efficiency. However, adversaries can easily inject abnormal messages into the CAN bus remotely to affect vehicle driving safety. Existing anomaly detection methods only focus on specific vehicle models and have a limited range of applications across different vehicles. To address this challenge, this paper proposes a Domain Adversarial training-based AutoEncoder (DAdAE) for unsupervised CAN anomaly detection. The advantages of our approach are: 1) detect variant attack scenarios on different car models 2) does not require labeled data 3) works well even with a limited dataset. The effectiveness of the proposed model is evaluated on the survival dataset, and the experiment results show that the DAdAE model improves the overall f1 score significantly, compared to other unsupervised models.

Recent year has brought considerable advancements in Electric Vehicles (EVs) and associated infrastructures/communications. Intrusion Detection Systems (IDS) are widely deployed for anomaly detection in such critical infrastructures. This paper presents an Interpretable Anomaly Detection System (RX-ADS) for intrusion detection in CAN protocol communication in EVs. Contributions include: 1) Feature Extractor; 2) Anomaly Detection System; and 3) Explanation Generator for detected anomalies. The presented approach was tested on two benchmark CAN datasets: OTIDS and Car Hacking. The anomaly detection performance of RX-ADS was compared against the state-of-the-art approaches on these datasets: HIDS and GIDS. The RX-ADS approach showed comparable performance to the HIDS approach on OTIDS dataset and outperformed HIDS and GIDS approaches on Car Hacking dataset. Further, the proposed approach was able to generate explanations for detected abnormal behaviors arising from various intrusions. These explanations were later validated by information used by domain experts to detect anomalies. Other advantages of RX-ADS include: 1) the method can be trained on unlabeled data; 2) explanations help experts in understanding anomalies and root course analysis, and also help with AI model debugging and diagnostics, ultimately improving user trust in AI systems.

In critical infrastructures, timely detection of anomalies is essential to detect failures, avoid catastrophic damages, and improve resilience. Neural Network models are one of the state-of-the-art approaches used for anomaly detection. Among Neural Network architectures used these days, Autoencoders (AEs) have gained significant attention due to their advantages such as unsupervised learning, dimensionality reduction, non-linear feature extraction, the ease of integration with other neural network algorithms, and ease of use. Therefore, in this paper, we present: 1) anomaly detection and types of anomaly detection, 2) recent advancements in AEs typically used in anomaly detection, 3) AE-based Anomaly Detection (AE-AD) in selected critical infrastructures such as smart grids, intelligent transportation systems, and smart buildings, and 4) future research opportunities. We hope that this systematic survey of AE-based anomaly detection approaches will help the community prioritize research efforts to address pressing issues in critical infrastructures.

Clustering is an AI technique that has been successfully applied to the abundance of unlabelled real-world data for revealing hidden patterns and knowledge extraction. Deep Embedded Clustering (DEC) is a deep Autoencoder (AE) based model that learns feature representations and cluster assignments simultaneously. DEC learns the mapping from input data to a low-dimensional embedded space through joint optimization of feature transformation and clustering. Our previous work demonstrates how adding residual connections to deep AEs (RAEs) reduces the performance degradation of learned features when performing downstream classification on learned features. Further, it evidenced that RAE has improved unsupervised feature learning capability compared to AE. In this paper, we are evaluating the effect of residual connections in the context of Deep Embedded Clustering (DEC), which we refer to as RDEC. RDEC was compared against regular DEC. We considered various numbers of hidden layers and several bench-mark datasets: MNIST, Fashion MNIST, Reuters, and Human activity recognition. When increasing the depth of the neural network gradually, the presented RDEC showed up to 56% of less performance degradation compared to DEC. Further, the distribution of clustering accuracies showed that the presented RDEC outperforms DEC when comparing the accuracy variance and mean accuracy.

Despite the performance of state-of-the-art Artificial Intelligence (AI) systems, some sectors hesitate to adopt AI because of a lack of trust in these systems. This attitude is prevalent among high-risk areas, where there is a reluctance to remove humans entirely from the loop. In these scenarios, Augmentation provides a preferred alternative over complete Automation. Instead of replacing humans, AI Augmentation uses AI to improve and support human operations, creating an environment where humans work side by side with AI systems. In this paper, we discuss how AI Augmentation can provide a path for building Trustworthy AI. We exemplify this approach using Robot Teleoperation. We lay out design guidelines and motivations for the development of AI Augmentation for Robot Teleoperation. Finally, we discuss the design of a Robot Teleoperation testbed for the development of AI Augmentation systems.

Artificial Intelligence (AI) is influencing almost all areas of human life. Even though these AI-based systems frequently provide state-of-the-art performance, humans still hesitate to develop, deploy, and use AI systems. The main reason for this is the lack of trust in AI systems caused by the deficiency of transparency of existing AI systems. As a solution, “Trustworthy AI” research area merged with the goal of defining guidelines and frameworks for improving user trust in AI systems, allowing humans to use them without fear. While trust in AI is an active area of research, very little work exists where the focus is to build human trust to improve the interactions between human and AI systems. In this paper, we provide a concise survey on concepts of trustworthy AI. Further, we present trustworthy AI development guidelines for improving the user trust to enhance the interactions between AI systems and humans, that happen during the AI system life cycle.

Deep brain stimulation (DBS) is well recognized as an effective treatment for symptoms of movement disorders such as Parkinson's disease (PD), Essential Tremor, and dystonia. The selection of the appropriate contact on the DBS lead for optimal clinical efficacy can be challenging, particularly when considering directional leads. Electroencephalograms (EEG) and electrocorticography has been utilized to better understand the pathophysiology of PD but a methodology to provide an objective biomarker of effective stimulation has yet to be developed. Using machine learning techniques for feature extraction and classification, we contrast high resolution EEG captured during DBS against its resting state counterpart with the DBS off. We demonstrate, using 16 patients under DBS treatment for movement disorders, EEG's informative capacity to detect both effective DBS and the region undergoing stimulation.

A viable solution for increasing fuel efficiency in vehicles is optimizing driver behavior. In our previous work, we proposed a data-driven Intelligent Driver System (IDS), which calculated an optimal driver behavior profile for a fixed route. During operation, the optimal behavior was prompted to the drivers to guide their behavior toward improving fuel efficiency. This system was proposed for fleet vehicles mainly because a small increase in fuel efficiency of fleet vehicles has a significant impact on the economy. The system was tested on a portion of the fleet's route (12km) and achieved 9-20% of fuel saving. One limitation of the IDS was that the prompted behavior profile was the same for all drivers. However, the approach of driving is significantly different from driver to driver. Therefore, it is important to capture those differences in the optimal behavior profile creation and prompting. This paper presents the first steps of a modified IDS that incorporates different approaches of drivers in optimal behavior profile creation. This work has three main components: 1) analyzing the capability of scaling our previously proposed IDS to the complete route of the fleet, 2) assessing the capability of identifying different types of driver behavior from data, and 3) proposing an IDS framework for integrating different driver behavior in optimizing driver behavior. Experimental results showed that the existing IDS was able to achieve 26-37% estimated fuel savings on the complete route. Conclusions of the paper are: 1)the existing IDS scaled to longer routes, and 2) It is possible to identify different driver behavior using data.

Crowdsourcing applications are proven to be a promising tool to gather valuable information, which can be used for a wide range of tasks, such as ensuring public safety. Traffic data collected using these applications have been used for efficient evacuation planning in large cities. In this paper, we propose to use regression-based machine learning methods to predict hourly taxi rides for a given location in a target day of week and month. The presented method can be used for the following purposes: 1) Predicting the number of taxi rides for a given location at a given time, 2) Identifying hot spots in a city, 3) Getting a rough count of the population density at a given location at a targeted hour, and 4) Planing evacuation routes for possible disasters. The presented approach has potential use for resource planning and evacuation in large cities. The Taxi and Limousine Commission (TLC) trip record data collected from 2017 to 2018 was used for this experiment. It was found that random forest regression can successfully predict hourly taxi drop-offs for a given taxi zone as well as for the entire city of New York.

Characterizing communications in smart-grid distributed control systems is fundamental for understanding the expected behavior and identify abnormal scenarios. In this paper, we present a stochastic data-driven approach to model the the communication network in smart-grid systems. Our approach uses Mixture Poisson distributions to model the packet communication between the network devices. The network is modeled using a directed graph, where each edge represents a Poisson distribution of the packets being transmitted. Parameters are learned using mini-batch Expectation Maximization in order to scale to large datasets. The advantages of the presented approach are 1) unsupervised data-driven discovery of representative communication patterns, 2) intuitive visualization of the expected behavior, 3) scalability to large datasets, and 4) coherent and interpretable model. Tests were conducted in a simulated SCADA microgrid distributed control system environment.

The deep self-organizing map (DSOM) was introduced to embed hierarchical feature abstraction capability to self-organizing maps (SOMs). This paper presents an extended version of the original DSOM algorithm (E-DSOM). E-DSOM enhances the DSOM in two ways-learning algorithm is modified to be completely unsupervised, and architecture is modified to learn features of different resolution in hidden layers. E-DSOM has three main advantages over the original DSOM: 1) improved classification accuracy; 2) improved generalization capability; and 3) need of fewer sequential layers (reduced training time). E-DSOM was tested on benchmark and real-world datasets and was compared against DSOM, SOM, sStacked autoencoder (AE), and stacked convolutional autoencoder (CAE). Experimental results showed that the E-DSOM outperformed DSOM with improvements of classification accuracy up to 15% while saving training time up to 19% on all datasets. Moreover, E-DSOM evidenced better generalization capability compared to the DSOM by showing superior performance on all datasets with induced noise. Further, E-DSOM showed comparable performance to the AE and the CAE while outperforming them on two datasets.

Visual data mining facilitates the involvement of domain experts in the data mining processes. The effectiveness of visual data mining is especially dominant when paired with unsupervised methods due to the abundance of unlabeled data. Deep Self-Organizing Maps (DSOMs) are unsupervised learning architectures capable of high level feature abstraction. In this paper, we analyze the effectiveness of using DSOMs for visual data mining. DSOM's visual data mining capability was evaluated using the following visual data explorations methodologies: 1) U-Matrix, 2) hit maps and 3) data histograms. In comparison with traditional single layered SOM architectures, experimental results showed that DSOMs produced more accurate visual representations of the underlying data distributions. Therefore, DSOM is a viable method for generating easily understandable visual representations of high-dimensional complex datasets. These visual representations can be powerful tools in the real world, leading to better understanding of systems and thus enabling the design of better algorithms for control and monitoring.

Despite the growing popularity of modern machine learning techniques (e.g, Deep Neural Networks) in cyber-security applications, most of these models are perceived as a black-box for the user. Adversarial machine learning offers an approach to increase our understanding of these models. In this paper we present an approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems (IDSs) An adversarial approach is used to find the minimum modifications (of the input features) required to correctly classify a given set of misclassified samples. The magnitude of such modifications is used to visualize the most relevant features that explain the reason for the misclassification. The presented methodology generated satisfactory explanations that describe the reasoning behind the mis-classifications, with descriptions that match expert knowledge. The advantages of the presented methodology are: 1) applicable to any classifier with defined gradients. 2) does not require any modification of the classifier model. 3) can be extended to perform further diagnosis (e.g. vulnerability assessment) and gain further understanding of the system. Experimental evaluation was conducted on the NSL-KDD99 benchmark dataset using Linear and Multilayer perceptron classifiers. The results are shown using intuitive visualizations in order to improve the interpretability of the results.

Cyber-Physical Systems (CPSs)have become ubiquitous in recent years and has become the core of modern critical infrastructure and industrial applications. Therefore, ensuring security is a prime concern. Due to the success of Deep Learning (DL)in a multitude of domains, development of DL based CPS security applications have received increased interest in the past few years. Developing generalized models is critical since the models have to perform well under threats that they havent trained on. However, despite the broad body of work on using DL for ensuring the security of CPSs, to our best knowledge very little work exists where the focus is on the generalization capabilities of these DL applications. In this paper, we intend to provide a concise survey of the regularization methods for DL algorithms used in security-related applications in CPSs and thus could be used to improve the generalization capability of DL based cyber-physical system based security applications. Further, we provide a brief insight into the current challenges and future directions as well.