Supervisory Control and Data Acquisition (SCADA) systems are the underlying monitoring and control components of critical infrastructures, such as power, telecommunication, transportation, pipelines, chemicals and manufacturing plants. Legacy SCADA systems operated on isolated networks, that made them less exposed to Internet threats. However, the increasing connection of SCADA systems to the Internet, as well as corporate networks, introduces severe security issues. Security considerations for SCADA systems are gaining higher attention, as the number of security incidents against these critical infrastructures is increasing. In this survey, we provide an overview of the general SCADA architecture, along with a detailed description of the SCADA communication protocols. Additionally, we discuss certain high-impact security incidents, objectives, and threats. Furthermore, we carry out an extensive review of the security proposals and tactics that aim to secure SCADA systems. We also discuss the state of SCADA system security. Finally, we present the current research trends and future advancements of SCADA security.

When fully implemented, sixth generation (6G) wireless systems will constitute intelligent wireless networks that enable not only ubiquitous communication but also high-accuracy localization services. They will be the driving force behind this transformation by introducing a new set of characteristics and service capabilities in which location will coexist with communication while sharing available resources. To that purpose, this survey investigates the envisioned applications and use cases of localization in future 6G wireless systems, while analyzing the impact of the major technology enablers. Afterwards, system models for millimeter wave, terahertz and visible light positioning that take into account both line-of-sight (LOS) and non-LOS channels are presented, while localization key performance indicators are revisited alongside mathematical definitions. Moreover, a detailed review of the state of the art conventional and learning-based localization techniques is conducted. Furthermore, the localization problem is formulated, the wireless system design is considered and the optimization of both is investigated. Finally, insights that arise from the presented analysis are summarized and used to highlight the most important future directions for localization in 6G wireless systems.

The emergence of next-generation Internet-of-Things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

Achieving the fifth-generation (5G) vision will introduce new technology innovations and substantial changes in delivering cutting-edge applications and services in current mobile and cellular networks. The Cloud Radio Access Network (C-RAN) concept emerged as one of the most compelling architectures to meet the requirements of the 5G vision. In essence, C-RAN provides an advanced mobile network architecture which can leverage challenging features such as network resource slicing, statistical multiplexing, energy efficiency, and high capacity. The realization of C-RAN is achieved by innovative technologies such as the software-defined networking (SDN) and the network function virtualization (NFV). While SDN technology brings the separation of the control and data planes in the playground, supporting thus advanced traffic engineering techniques such as load balancing, the NFV concept offers high flexibility by allowing network resource sharing in a dynamic way. Although SDN and NFV have many advantages, a number of challenges have to be addressed before the commercial deployment of 5G implementation. In addition, C-RAN introduces a new layer in the mobile network, denoted as the fronthaul, which is adopted from the recent research efforts in the fiber-wireless (Fi-Wi) paradigm. As the fronthaul defines a link between a baseband unit (BBU) and a remote radio unit (RRU), various technologies can be used for this purpose such as optical fibers and millimeter-wave (mm-wave) radios. In this way, several challenges are highlighted which depend on the technology used. In the light of the aforementioned remarks, this paper compiles a list of challenges and open issues of the emerging technologies that realize the C-RAN concept. Moreover, comparative insights between the current and future state of the C-RAN concept are discussed. Trends and advances of those technologies are also examined towards shedding light on the proliferation of 5G through the C-RAN concept.

The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

The Industrial Control Systems (ICS) are the underlying monitoring and control components of critical infrastructures, which consist of a number of distributed field devices, such as Programmable Logic Controllers (PLCs), Remote Terminal Units (RTUs) and Human Machine Interfaces (HMIs). As modern ICS are connected to the Internet, in the context of their digitalization as a part of the Internet of Things (IoT) domain, a number of security threats are introduced, whose exploitation can lead to severe consequences. Honeypots and honeynets are promising countermeasures that attract attackers and mislead them from hacking the real infrastructure, while gaining valuable information about the attack patterns as well as the source of the attack. In this work, we implement an interactive, proof-of concept ICS honeypot, which is based on Conpot, that is able to emulate a physical ICS device, by replicating realistic traffic from the real device. As the honeypot runs inside a Virtual Machine, it is possible to emulate the entire organization's ICS infrastructure, a fact that is very important for the security of the modern critical infrastructure. In order to assess the proposed honeypot, a real-life demonstration scenario was designed, which involves a hydro power plant. The honeypot architecture is provided, while the structural components are presented in detail.