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

Trellis Coding based on RLLPUM Codes for RFID Reader-to-Tag Channel

Abstract: Trellis coding techniques are simple graphical methodology in the encoder and decoder development in information theory. When this technique is applied to partial unit memory code (PUM) code, minimal trellis designs with less complex approach are identified using Shannon product theory. Combination of run-length limited (RLL) and PUM codes is called RLLPUM code, which is applicable to digital magnetic recording channels. Radio frequency identification (RFID) system contains reader-to-tag channel, which has similar properties as used in digital magnetic recording channels. In this paper, error detection and correction are considered with trellis coding techniques based on RLLPUM codes. Data storage is challenging topic with other competing technologies which provide a number of benefits. We propose a technique that allows the design of PUM codes with balanced RLL properties influenced with RFID reader-to-tag channel. From the results, error detection, correction and storage, which could be increased up to 50%, are compared

Secure Cyber-Physical Systems for Improving Transportation Facilities in Smart Cities and Industry 4.0

The main aim of this strategic research proposal is to develop a model of secure transportation system using efficient CPS which not only reduce the unnecessary accident rates but also increase safety system that enhances the livability of smart cities and Industry 4.0. Although the main focus is efficient security solutions, dynamic and intelligent approaches of the future security solutions will be able to detect the evolving threats and cyberattacks during the data or signal transmission between the users and service providers

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