Internet-of-Things (IoT) ecosystems tend to grow both in scale and complexity, as they consist of a variety of heterogeneous devices that span over multiple architectural IoT layers (e.g., cloud, edge, sensors). Further, IoT systems increasingly demand the resilient operability of services, as they become part of critical infrastructures. This leads to a broad variety of research works that aim to increase the resilience of these systems. In this article, we create a systematization of knowledge about existing scientific efforts of making IoT systems resilient. In particular, we first discuss the taxonomy and classification of resilience and resilience mechanisms and subsequently survey state-of-the-art resilience mechanisms that have been proposed by research work and are applicable to IoT. As part of the survey, we also discuss questions that focus on the practical aspects of resilience, e.g., which constraints resilience mechanisms impose on developers when designing resilient systems by incorporating a specific mechanism into IoT systems.
This article is devoted to the definition of the most important combinations of objects in critical network infrastructures. This study was carried out using the example of the Russian gas transmission network. Since natural gas is widely used in the energy sector, the gas transmission network can be exposed to terrorist threats, and the actions of intruders can be directed at both gas fields and gas pipelines. A defender–attacker model was proposed to simulate attacks. In this model, the defender solves the maximum flow problem to satisfy the needs of gas consumers. By excluding gas pipelines, the attacker tries to minimize the maximum flow in the gas transmission network. Russian and European gas transmission networks are territorially very extensive and have a significant number of mutual intersections and redundant pipelines. Therefore, one of the approaches to inflicting maximum damage on the system is modeled as an attack on a clique. A clique in this study is several interconnected objects. The article presents the list of the most interconnected sections of main gas pipelines, the failure of which can cause the greatest damage to the system in the form of a gas shortage among consumers. Conclusions were drawn about the applicability of the maximum clique method for identifying the most important objects in network critical infrastructures.
With the increase in cybercrimes over the last few years, a growing realization for the need for cybersecurity has begun to be recognized by the nation. Unfortunately, being aware that cybersecurity is something you need to worry about and knowing what steps to take are two different things entirely. In the United States, the National Institute of Standards and Technology (NIST) developed the Cyber Security Framework (CSF) to assist critical infrastructures in determining what they need in order to secure their computer systems and networks. While aimed at organizations, much of the guidance provided by the CSF, especially the basic functions it identifies, are also valuable for communities attempting to put together a community cybersecurity program.
Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.
This article is dedicated to the development of a software model with a Graphical User Interface (GUI) to simulate the process of ensuring information and cyber security of information systems (IS) of Critical Infrastructure objects (CI) based on the analytical model developed by the author of this article. The specified software model with a GUI makes it possible, using the controls located on the main panel, to set the input parameters of the simulated object and observe its output characteristics using appropriate visualization elements such as special windows for displaying calculated numerical values of the main characteristics of the systems under study.
The unified methodological basis of information and analytical support of socio-economic security network-centric control in the region is proposed. The problem of regional security support is discussed at the level of risk-management of critical infrastructure resilience violation of the socio-economic systems. The methodology and tools for its implementation are aimed to information and analytical support of situational centers functioning in the region.