Internet of Vehicles (IoV) is gradually becoming popular, but it also brings more opportunities for malware intrusion. The intrusion of malware into IoV will cause a series of security issues and increase the incidence of road accidents. Therefore, the suppressing measures to combat the spread of malware in IoV will be fundamental and urgent. To address this critical issue, this paper proposes a fractional-order IoV (FIOV) to investigate malware propagation patterns in Road Side Unit (RSU) and Vehicles. To accurately reflect the actual spread of malware, the traffic density, the channel fading and the actual connectivity are considered in mathematical model. Then, the model-based optimal treatment and quarantine control strategy is derived by optimal control theory. Additionally, a novel model-free FIOV multi-agent soft actor-critic (FIOV-MASAC) approach is first proposed to suppress the malware propagation in IoV. Simulation experiments demonstrate that the proposed FIOV-MASAC approach exhibits better learning ability compared to other reinforcement learning (RL) algorithms.Note to Practitioners—Frequent attacks by malware on IoV are recognized as being challenging to prevent, with these attacks posing threats to data security and potentially resulting in traffic accidents and vehicle malfunctions. In response, a novel mathematical model has been introduced within this study to better predict the propagation trends of malware in IoV, effectively managing its spread within the vehicular network systems. While RL methods have been extensively utilized in the domain of control systems, it is noted that current RL methods depend on rich experience pools, rendering them inapplicable to more complex systems without adaptation. To address this, an effective and pragmatic RL algorithm has been devised in this study. This algorithm, devoid of the requirement for complex model establishment, is capable of intelligently learning and adjusting to the sophisticated environment of IoV, thereby effectively countering the propagation of malware. It should be highlighted that the RL method proposed herein is applicable to the majority of epidemic systems, enabling the achievement of stable control while substantially minimizing control expenditures. The integration of this method is anticipated to augment the security and robustness of IoV in the face of malware attacks.

The Internet of Underwater Things (IoUT) relies on wireless communication devices that are arranged in an open underwater environment and can interact with other devices through acoustic communication technology. However, due to their limited resources and open underwater environment, IoUT has been suffering from a high risk of malware attacks. As the two main parts of IoUT, autonomous underwater vehicles (AUVs) and underwater wireless rechargeable sensor networks (UWRSNs) are more favored by attackers and can be directly attacked by malware, which can lead to the cross-propagation of malware when AUVs and UWRSNs exchange information. To mitigate that threat, there is an urgent need to study the propagation patterns of malware and control their spread. Therefore, we establish a mathematical model based on the fractional-order theoretical framework to investigate the malware propagation patterns in two coupled networks (UWRSNs and AUVs). Then, we combine immune, charging, and quarantine delays in control and derive the optimal control strategies based on optimal control theory. Moreover, to improve the generality of control, we propose a machine learning (ML) controller that combines ML [e.g., deep neural network (DNN) and random forest (RF)] with control theory. Ultimately, our simulation experiments show that the proposed optimal control strategy is more effective in inhibiting the spread of malware while obtaining the minimum control cost under different fractional-order scenes. At the same time, the ML-based control results are close to the optimal control.

Visually identifying effective bio-markers from human brain networks poses non-trivial challenges to the field of data visualization and analysis. Existing methods in the literature and neuroscience practice are generally limited to the study of individual connectivity features in the brain (e.g., the strength of neural connection among brain regions). Pairwise comparisons between contrasting subject groups (e.g., the diseased and the healthy controls) are normally performed. The underlying neuroimaging and brain network construction process is assumed to have 100% fidelity. Yet, real-world user requirements on brain network visual comparison lean against these assumptions. In this work, we present MV^2Net, a visual analytics system that tightly integrates multi-variate multi-view visualization for brain network comparison with an interactive wrangling mechanism to deal with data uncertainty. On the analysis side, the system integrates multiple extraction methods on diffusion and geometric connectivity features of brain networks, an anomaly detection algorithm for data quality assessment, single- and multi-connection feature selection methods for bio-marker detection. On the visualization side, novel designs are introduced which optimize network comparisons among contrasting subject groups and related connectivity features. Our design provides level-of-detail comparisons, from juxtaposed and explicit-coding views for subject group comparisons, to high-order composite view for correlation of network comparisons, and to fiber tract detail view for voxel-level comparisons. The proposed techniques are inspired and evaluated in expert studies, as well as through case analyses on diffusion and geometric bio-markers of certain neurology diseases. Results in these experiments demonstrate the effectiveness and superiority of MV^2Net over state-of-the-art approaches.

Visualizing massive scale human movement in cities plays an important role in solving many of the problems that modern cities face (e.g., traffic optimization, business site configuration). In this article, we study a big mobile location dataset that covers millions of city residents, but is temporally sparse on the trajectory of individual user. Mapping sparse trajectories to illustrate population movement poses several challenges from both analysis and visualization perspectives. In the literature, there are a few techniques designed for sparse trajectory visualization; yet they do not consider trajectories collected from mobile apps that possess long-tailed sparsity with record intervals as long as hours. This article introduces UrbanMotion, a visual analytics system that extends the original wind map design by supporting map-matched local movements, multi-directional population flows, and population distributions. Effective methods are proposed to extract and aggregate population movements from dense parts of the trajectories leveraging their long-tailed sparsity. Both characteristic and anomalous patterns are discovered and visualized. We conducted three case studies, one comparative experiment, and collected expert feedback in the application domains of commuting analysis, event detection, and business site configuration. The study result demonstrates the significance and effectiveness of our system in helping to complete key analytics tasks for urban users.

With Virtual Reality (VR) devices and content getting increasingly popular, understanding user behaviors in virtual environment is important for not only VR product design but also user experience improvement. In VR applications, the head movement is one of the most important user behaviors, which can reflect a user's visual attention, preference, and even unique motion pattern. However, to the best of our knowledge, no dataset containing this information is publicly available. In this paper, we present a head tracking dataset composed of 48 users (24 males and 24 females) watching 18 sphere videos from 5 categories. We carefully record how users watch the videos, how their heads move in each session, what directions they focus, and what content they can remember after each session. Based on this dataset, we show that people share certain common patterns in VR spherical video streaming, which are different from conventional video streaming. We believe the dataset can serve good resource for exploring user behavior patterns in VR applications.