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

Fog computing security: a review of current applications and security solutions

Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems

Project BeARCAT : Baselining, Automation and Response for CAV Testbed Cyber Security : Connected Vehicle & Infrastructure Security Assessment

Connected, software-based systems are a driver in advancing the technology of transportation systems. Advanced automated and autonomous vehicles, together with electrification, will help reduce congestion, accidents and emissions. Meanwhile, vehicle manufacturers see advanced technology as enhancing their products in a competitive market. However, as many decades of using home and enterprise computer systems have shown, connectivity allows a system to become a target for criminal intentions. Cyber-based threats to any system are a problem; in transportation, there is the added safety implication of dealing with moving vehicles and the passengers within

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified