Autonomous Vehicles:The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication

The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication

Emerging Trends in Radar:long-range surveillance in North Polar Region

Sky-wave over-the-horizon radar (OTHR) relies on bouncing radio waves off the ionosphere to achieve long-range surveillance, even beyond the Earth's curvature. A critical component of OTHR is the real-time frequency monitoring system (FMS), which must continuously adjust to accommodate the dynamic ionospheric conditions, particularly in high-latitude and polar regions. To maintain consistent detection of distant targets, OTHR systems must periodically adjust operating frequencies and elevation angles in response to these fluctuating conditions. In this context, the Assimilation Canadian High Arctic Ionospheric Model (A-CHAIM) was developed to represent the short-term variability and unique features of the high-latitude and polar ionosphere. This model serves as a cutting-edge tool for real-time ionospheric modeling in these challenging regions; however, inconsistent availability and distribution of observations ultimately limits the scales of structuring that the model can capture. To address these limitations, Defence Research and Development Canada (DRDC) is working on several enhancements to improve A-CHAIM's performance and reliability in the futur

Decentralized Multi-Layered Architecture to Strengthen the Security in the Internet of Things Environment Using Blockchain Technology

Smart devices are connected to IoT networks and the security risks are substantial. Using blockchain technology, which is decentralized and distributed, 5G-enabled IoT networks might be able to tackle security issues. In order to simplify the implementation and security of IoT networks, we propose a multi-level blockchain security model. As part of the multi-level architecture, the communication between levels is facilitated by clustering. IoT networks define unknown clusters with applications that utilize the evolutionary computation method coupled with anatomy simulation and genetic methodologies. Authentication and authorization are performed locally by the super node. The super node and relevant base stations can communicate using local private blockchain implementations. A blockchain improves security and enhances trustworthiness by providing network authentication and credibility assurance. The proposed model is developed using the open-source Hyperledger Fabric blockchain platform. Stations communicate securely using a global blockchain. Compared to the earlier reported clustering algorithms, simulations demonstrate the efficacy of the proposed algorithm. In comparison with the global blockchain, the lightweight blockchain is more suitable for balancing network throughput and latency

Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments

In order to reach the highest level of automation, autonomous vehicles (AVs) are required to be aware of surrounding objects and detect them even in adverse weather. Detecting objects is very challenging in sandy weather due to characteristics of the environment, such as low visibility, occlusion, and changes in lighting. In this paper, we considered the You Only Look Once (YOLO) version 5 and version 7 architectures to evaluate the performance of different activation functions in sandy weather. In our experiments, we targeted three activation functions: Sigmoid Linear Unit (SiLU), Rectified Linear Unit (ReLU), and Leaky Rectified Linear Unit (LeakyReLU). The metrics used to evaluate their performance were precision, recall, and mean average precision (mAP). We used the Detection in Adverse Weather Nature (DAWN) dataset which contains various weather conditions, though we selected sandy images only. Moreover, we extended the DAWN dataset and created an augmented version of the dataset using several augmentation techniques, such as blur, saturation, brightness, darkness, noise, exposer, hue, and grayscale. Our results show that in the original DAWN dataset, YOLOv5 with the LeakyReLU activation function surpassed other architectures with respect to the reported research results in sandy weather and achieved 88% mAP. For the augmented DAWN dataset that we developed, YOLOv7 with SiLU achieved 94% mAP

Filtering using a tree-based estimator

Within this paper a new framework for Bayesian tracking is presented, which approximates the posterior distribution at multiple resolutions. We propose a tree-based representation of the distribution, where the leaves define a partition of the state space with piecewise constant density. The advantage of this representation is that regions with low probability mass can be rapidly discarded in a hierarchical search, and the distribution can be approximated to arbitrary precision. We demonstrate the effectiveness of the technique by using it for tracking 3D articulated and nonrigid motion in front of cluttered background. More specifically, we are interested in estimating the joint angles, position and orientation of a 3D hand model in order to drive an avatar

Estimating 3D hand pose using hierarchical multi-label classification

This paper presents an analysis of the design of classifiers for use in a hierarchical object recognition approach. In this approach, a cascade of classifiers is arranged in a tree in order to recognize multiple object classes. We are interested in the problem of recognizing multiple patterns as it is closely related to the problem of locating an articulated object. Each different pattern class corresponds to the hand in a different pose, or set of poses. For this problem obtaining labelled training data of the hand in a given pose can be problematic. Given a parametric 3D model, generating training data in the form of example images is cheap, and we demonstrate that it can be used to design classifiers almost as good as those trained using non-synthetic data. We compare a variety of different template-based classifiers and discuss their merits

Hand tracking using a quadric surface model and Bayesian filtering

Within this paper a technique for model-based 3D hand tracking is presented. A hand model is built from a set of truncated quadrics, approximating the anatomy of a real hand with few parameters. Given that the projection of a quadric onto the image plane is a conic, the contours can be generated efficiently. These model contours are used as shape templates to evaluate possible matches in the current frame. The evaluation is done within a hierarchical Bayesian filtering framework, where the posterior distribution is computed efficiently using a tree of templates. We demonstrate the effectiveness of the technique by using it for tracking 3D articulated and non-rigid hand motion from monocular video sequences in front of a cluttered background