Software-Defined Networking (SDN) is an emerging network architecture that addresses the limitation of the traditional network by providing centralized management through a central controller that decouples the control and data planes. However, this development has made the controller a severe target for malicious users to execute attacks such as Distributed Denial of Service (DDoS) attacks. Several schemes have been proposed to mitigate DDoS attacks in SDN, but the challenges still exist. This paper proposes a DDoS mitigation scheme for SDN to ensure accurate attack detection and efficient network resource utilization. The scheme employs two stages: a bandwidth control mechanism and Extreme Gradient Boosting (XGBoost) Algorithm. The bandwidth control mechanism utilizes an adaptive bandwidth profile-based threshold and bandwidth control algorithm that trigger the XGBoost algorithm in case of threshold violations. The use of multiple bandwidth profiles in setting the threshold ensures the threshold’s adaptivity to consider the network traffic variation and reduce the packets drop ratio, which shows an outstanding result. The XGBoost algorithm classifies network traffic flow that violates a set threshold into normal or abnormal traffic. We evaluated the performance of our scheme using CICDDoS2019, NSL-KDD, and CAIDA datasets. Furthermore, we validated our proposed solution in real-time with the SDN environment. The results obtained show that our scheme protects SDN against DDoS attacks with high accuracy, low error, and efficient utilization of the network resources. The proposed system achieved 99.9% accuracy in detecting DDoS attacks with a low false-positive rate of 0.0002% in SDN.

The evaluation of data breaches and cybersecurity risks has not yet been formally addressed in modern business systems. There has been a tremendous increase in the generation, usage and consumption of industrial and business data as a result of smart and computational intensive software systems. This has resulted in an increase in the attack surface of these cyber systems. Consequently, there has been a consequent increase in the associated cybersecurity risks. However, no significant studies have been conducted that examine, compare, and evaluate the approaches used by the risk calculators to investigate the data breaches. The development of an efficient cybersecurity solution allows us to mitigate the data breaches threatened by the cybersecurity risks such as cyber-attacks against database storage, processing and management. In this paper, we develop a comprehensive, formal model that estimates the two components of security risks: breach cost and the likelihood of a data breach within 12 months. The data used in this model are taken from the industrial business report, which provides the necessary information collected and the calculators developed by the major organizations in the field. This model integrated with the cybersecurity solution uses consolidated factors that have a significant impact on the data breach risk. We propose mathematical models of how the factors impact the cost and the likelihood. These models allow us to conclude that results obtained through the models mitigate the data breaches in the potential and future business system dynamically.

With the rapid growth of internet-connected devices and their resource-constrained capabilities, the current authentication mechanisms are unable to meet the complex IoT application requirements, such as in the Industrial Internet of Things (IIoT), due to the increased computation, communication, and storage overhead arising from these mechanisms. In the IIoT, machine-to-machine (M2M) communication is an underlying technology where devices (e.g., sensors, actuators, and controllers) can be enabled to exchange information autonomously; thus, the massive data generated by these devices can increase latency, network congestion, and the complexity of security management. Message queue telemetry transport (MQTT) is one of the promising M2M protocols used in the IoT that could encounter such issues because it relies on a central broker in the cloud and implements a heavyweight authentication mechanism based on TLS. Therefore, this paper proposes an MQTT architecture with multi-tier brokers based on fog computing, where each broker is deployed with an authentication manager. In addition, the paper presents a lightweight mutual authentication scheme based on hash function and XOR operation. Comparing the results given in the benchmark, the overall performance of our scheme shows that storage and communication overheads are reduced to 89% and 23%, respectively. Furthermore, our system can resist against several cyberattacks and provide scalability.

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.

Sixth-generation (6G)-based communications have many applications and are emerging as a new system to utilize existing vehicles and communication devices in autonomous vehicles (AVs). Electric vehicles and AVs not supporting the integration of intelligent cybersecurity will become vulnerable, and their internal functions, features, and devices providing services will be damaged. This paper presents an intelligent cybersecurity model integrating intelligent features according to the emerging 6G-based technology based on evolving cyberattacks. The model’s novel design was developed using the necessary algorithms to provide quick and proactive decisions with intelligent cybersecurity based on 6G (IC6G) policies when AVs face cyberattacks. In this model, network security algorithms incorporating intelligent techniques are developed using applied cryptography. Money transaction handling services implemented in an AV are considered an example to determine the security and intelligence level depending on the IC6G policies. Intelligence, complexity, and energy efficiency (EE) are assessed. Finally, we conclude that the model results are effective for intelligently detecting and preventing cyberattacks on AVs.

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.