Security Risk Model Research Articles

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Spatial‐temporal security stability analysis and regulation against different types of attacks on industrial control systems

AbstractIn this article, reaction‐diffusion phenomena are considered in the security risk modeling of industrial control networks represented in the spatial‐temporal domain. Different types of cyber‐physical attacks, including TDS (time delay switch), FDI (false data injection), and DoS (denial of service), are modeled within a comprehensive parameteric‐varying parabolic partial differential state‐space structure. A theorem is proposed to ensure that the system is asymptotically stable with prescribed performance, which guarantees stability regardless of the various types of attacks. Finally, corresponding examples of risk control design against the different threats on industrial control networks are simulated to demonstrate the effectiveness of the results proposed in this article.

Cyber Security Risk Modeling in Distributed Information Systems

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach.

Evaluation of the Cyber Security Risk Models (CSRM) in Cloud Computing

Many devices in the Internet of Things (IoT) ecosystem may be susceptible to cyberattacks due to their diverse nature and lack of standardization. Resource-constrained IoT devices include sensor nodes, smart gadgets, and wearable devices. An organization's RAP (Risk Assessment Process) integrates the evaluation of hazards that are linked to all its resources, as well as the evaluation and prioritization of these risks. It is crucial to begin the risk management process with an accurate and thorough risk assessment. The Cyber Security Risk Models (CSRMs) in Cloud Computing are examined in this research. To understand the uniqueness of IoT systems and why present risk assessment methodologies for IoT are ineffective, it is necessary to understand the current state of risk assessment for IoT. There are constraints to periodic evaluations IoT due to device interoperability. Continuous testing of IoT solutions is thus essential.

Security Risk Models for Cyber Insurance

Security Risk Models for Cyber Insurance

Security Risk Level Prediction of Carbofuran Pesticide Residues in Chinese Vegetables Based on Deep Learning.

The supervision of security risk level of carbofuran pesticide residues can guarantee the food quality and security of residents effectively. In order to predict the potential key risk vegetables and regions, this paper constructs a security risk assessment model, combined with the k-means++ algorithm, to establish the risk security level. Then the evaluation index value of the security risk model is predicted to determine the security risk level based on the deep learning model. The model consists of a convolutional neural network (CNN) and a long short-term memory network (LSTM) optimized by an arithmetic optimization algorithm (AOA), namely, CNN-AOA-LSTM. In this paper, a comparative experiment is conducted on a small sample data set of independently constructed security risk assessment indicators. Experimental results show that the accuracy of the CNN-AOA-LSTM prediction model based on attention mechanism is 6.12% to 18.99% higher than several commonly used deep neural network models (gated recurrent unit, LSTM, and recurrent neural networks). The prediction model proposed in this paper provides scientific reference to establish the priority order of supervision, and provides forward-looking supervision for the government.

Quantitative Security Risk Modeling and Analysis with RisQFLan

Domain-specific quantitative modeling and analysis approaches are fundamental in scenarios in which qualitative approaches are inappropriate or unfeasible. In this paper, we present a tool-supported approach to quantitative graph-based security risk modeling and analysis based on attack-defense trees. Our approach is based on QFLan, a successful domain-specific approach to support quantitative modeling and analysis of highly configurable systems, whose domain-specific components have been decoupled to facilitate the instantiation of the QFLan approach in the domain of graph-based security risk modeling and analysis. Our approach incorporates distinctive features from three popular kinds of attack trees, namely enhanced attack trees, capabilities-based attack trees and attack countermeasure trees, into the domain-specific modeling language. The result is a new framework, called RisQFLan, to support quantitative security risk modeling and analysis based on attack-defense diagrams. By offering either exact or statistical verification of probabilistic attack scenarios, RisQFLan constitutes a significant novel contribution to the existing toolsets in that domain. We validate our approach by highlighting the additional features offered by RisQFLan in three illustrative case studies from seminal approaches to graph-based security risk modeling analysis based on attack trees.

Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented.

Novel security models, metrics and security assessment for maritime vessel networks

In the past few years, maritime vessels have become computerized and connected to the internet. However, with this technology, critical systems based on-board ships that manage the vessel’s navigation system, radar, cargo management system, etc have become more prone to cyber-attacks. Moreover, attackers are now becoming aware of the vulnerabilities associated with such maritime vessel systems. As a result, it is of vital importance to manage and secure the maritime vessel networks against cyber-attacks, but there is a lack of capabilities to efficiently manage the identifications of vulnerabilities, security risk assessment, and evaluate the effectiveness of countermeasures. Hence, we propose a novel framework and security risk modeling and assessment method to evaluate the security of maritime vessel networks. We develop (1) a security model for maritime vessels to capture probabilistic events, vulnerabilities, and network configurations of vessel components; (2) propose an approach to assess the network with a single function, multiple functions, and the inter-dependencies between the functions as attack goal(s); (3) adopt three well-defined security metrics with the proposed model to evaluate possible attacks and/or threats; and (4) compare the effectiveness of cyber-defense strategies based on different attack scenarios on the maritime vessel network. Besides, we perform sensitivity analysis based on temporal and permanent connections that are associated with the vessels’ systems to understand the effect of the connections on security. Our results demonstrate the applicability and usability of the proposed model for finding potential attack paths, assessing security, and mitigating the impact of cyber-attacks and threats on a vessel network.

A raw water security risk model for urban supply based on failure mode analysis

The provision of raw water to urban supply systems is a global endeavor, and a great challenge to water resource managers of large metropolitan regions. A sustainable supply requires the integrated management of water quantity and quality, as well as the system’s adaption to land use and climate changes (e.g., deforestation, droughts). The available water security models inform about the current security status of a system based on the assessment of quantity, quality and adaption indicators. But they barely include risk variables in the analysis, which could help to improve the security assessment considering the historical records of indicators or their future projections. In the present study, a new method is developed that couples a security assessment based on the rating of pressure indicators acting on a watershed, such as water demand, ordinary and accidental contaminants, droughts, and environmental settings (e.g., share of forest cover), with a risk assessment based on pressure properties such as severity, occurrence and detectability. The method is framed in the “Failure Mode and Effects Analysis”, and was applied to the Rio das Velhas system that supplies a portion of Belo Horizonte metropolitan region (Minas Gerais, Brazil) with >4 million inhabitants. The results exposed unacceptable risks for water demand, ordinary contaminants and droughts, because of their high severity and frequency. The water demand risks were explained by data on current (2019) water scarcity and population growth projections (until 2036) that are likely to raise water consumption. The ordinary contaminant risks were deduced from information on current deviations of water quality parameters (e.g., dissolved oxygen, turbidity) from legal thresholds, and records (since 2007) of stream water contamination by untreated domestic effluents. Finally, the drought risks were explained by information on the persistence of stream flows below a legal reference flow, coupled with the identification of many (23) dry years in the available stream flow record (48 years). Overall, the proposed model was efficient in the evaluation of water security risk in the basin. The results obtained for the study area help to act on the required mitigation through prioritization of risk attenuation. Given the simplicity and modest data requirements, the method is helpful to decision makers and suited for replication in other similar regions around the globe.

WITHDRAWN: Different simplified equations for FPGA-based cloud system development and mathematical models

Cloud data related to persons and organizations are in the cloud, security concerns about environment began to develop storage. Prevent security risks before entering the frame is wise, and do not know the security risks from the case where absolutely impossible to stop them. Specified assets and revenue growth is equally important issue especially in cloud security. This satisfies the need for a variety of Different Simplified Equations (DSE), such as security risk, asset allocation and revenue growth model. This paper analyses the security risks of distributed computing and mitigation measures. Instructions on how to attack common attacks on FPGA-based virtual machines in the cloud framework, and examine the major security risks and countermeasures. Virtual machines increase asset allocation and risk mitigation strategies and revenue models will be further considered.

Assessment of Information Security Impacts on Mobile Social Media

This paper proposed an indexed matrix to assess information security risks' impacts on mobile social media to help organisations address security risks effectively and efficiently. It developed and integrated a static social media information security risk model and a dynamic social media information flow model in the mobile environment to investigate the potential security risks in social networks. The pattern of security risks with three levels was identified using cluster analysis. An indexed matrix is also proposed to assess the information security impacts on social media via the relative-weigh quantitative method. The results can help to develop an effective and safe way to transmit information through social media. This research is the first attempt to integrate static information security threats into the dynamic social media information flow processes to assess the information security risk levels by developing a new indexed method. The theoretical and practical implications are presented.

Risk management-based security evaluation model for telemedicine systems

BackgroundInfectious diseases that can cause epidemics, such as COVID-19, SARS-CoV, and MERS-CoV, constitute a major social issue, with healthcare providers fearing secondary, tertiary, and even quaternary infections. To alleviate this problem, telemedicine is increasingly being viewed as an effective means through which patients can be diagnosed and medications prescribed by doctors via untact Thus, concomitant with developments in information and communication technology (ICT), medical institutions have actively analyzed and applied ICT to medical systems to provide optimal medical services. However, with the convergence of these diverse technologies, various risks and security threats have emerged. To protect patients and improve telemedicine quality for patient safety, it is necessary to analyze these risks and security threats comprehensively and institute appropriate countermeasures.MethodsThe security threats likely to be encountered in each of seven telemedicine service areas were analyzed, and related data were collected directly through on-site surveys by a medical institution. Subsequently, an attack tree, the most popular reliability and risk modeling approach for systematically characterizing the potential risks of telemedicine systems, was examined and utilized with the attack occurrence probability and attack success probability as variables to provide a comprehensive risk assessment method.ResultsIn this study, the most popular modelling method, an attack tree, was applied to the telemedicine environment, and the security concerns for telemedicine systems were found to be very large. Risk management and evaluation methods suitable for the telemedicine environment were identified, and their benefits and potential limitations were assessed.ConclusionThis research should be beneficial to security experts who wish to investigate the impacts of cybersecurity threats on remote healthcare and researchers who wish to identify new modeling opportunities to apply security risk modeling techniques.

The internet of things in healthcare: an overview, challenges and model plan for security risks management process

<p class="Abstract">The Internet of Things (IoT) has not been around for very long. However, since the notion of IoT introduced, most of IoT studies focused on a strategic level such as planning, architectures, standardization, and latest technologies, however, studies of risk management plan of IoT are still lacking. IoT has been widely used to link existing medical resources and provide reliable, effective and smart healthcare services to elderly and patients with chronic illnesses. However, a systematic process is missing when managing and anticipating the risk of IoT usage in healthcare. For this purpose, this paper extensively explores various IoT technologies used in health care services and its security challenges. As a result, IoT Security Risk Model for Healthcare is introduced to cater a complete process of risk management based on ISO/IEC 27005:2018 standard. It is believed that by having this model, it will emphasize on iterative IoT risk management process as it may increase the depth and detail of the assessment at each iteration.</p>

Security risk situation quantification method based on threat prediction for multimedia communication network

Multimedia communication network has gained remarkable popularity by a wide spectrum of users nowadays. It is easier that the potential threats conceal within the large-scale net flow of multimedia communication traffic. Once vulnerability exploitation occurs, the latent risk will be brought to the surface, causing a series of safety problems. Thus, the vulnerability analysis and threat prediction are becoming critical issues. Recently years, many investigations have been made. However, they are not sufficient. To provide a comprehensive view of the threat scenario and present a quantitative risk-aware approach, we propose a novel method for threat identification, and further we build a quantitative security risk model with it. Actually, two algorithms are proposed, namely dynamic Bayesian attack graph based threat prediction algorithm, and threat prediction based security risk quantification algorithm. The first algorithm aims to provide full prediction information with threat scenario. The second algorithm quantifies the threat in the first algorithm into the security risk from two levels: host and network. The examples indicate that our method is feasible and scalable, which enables a manager to quantify the risks of any identified threat or ongoing attack and to recognize the vulnerable multimedia devices to keep secure multimedia communication.

What Factors Influence Companies’ Successful Implementations of Technology Risk Management Systems?

During the initial literature review on this research question, areas of focus included the following: • Current qualitative and quantitative methodologies for technology risk analysis. • Business applications for expanding the use of qualitative and quantitative technology and security risk models. • Implementation of qualitative and quantitative technology and security risk analysis methodologies models by practitioners. Information Technology (IT) risk analysis has become be an integral part of the enterprise risk management systems in many organizations. However, many companies have struggled to effectively implement these systems. This has become a serious problem in many cases where governmental regulations, industry requirements, and even contractual language for doing business have increasingly included technology risk management obligations that companies must meet. Currently, technology risk management is not as mature a field as those like IT Audit or Information Security, which have had professional certification processes for over 23 years. Technology risk management, on the other hand, has had similar certifications for less than 10 years. As such, many of the current technology risk management practitioners have come from other fields, which has made it difficult to construct a common body of knowledge on which technology risk management systems can be built. In many cases, such factors, as well as others, are making it difficult to implement technology risk management systems. This research will seek to evaluate those factors in more detail to determine common ones that have the most impact on the success of technology risk management projects and make recommendations for overcoming the factors that limit the success of these projects.

The System Architecture of Smart Water Grid for Water Security

Architecture of smart water grid based on Internet of Things was proposed, mainly including hierarchy framework, technical system and function framework. The hierarchy framework is consists of real-time active sensing layer, interconnecting layer of water information interconnection layer, and smart decision layer. The technical system mainly contains the techniques,e.g.,the multi-carrier network composing and real-time transporting, intelligent storage of mass data and the seamless docking of multi-modules of the system, the coupled simulation of multi-dimensional water model, the evaluation and diagnosis of water security risk,the fast and dynamic forecasting model building of water pollution incidents, the multi-objective optimization and control technology of water quality and quantity of cascade reservoirs, the dynamic early warning technology in the unexpected water pollution incidents, the emergency disposal technology of the multi-scale, multi-type and multi-objective unexpected water pollution incidents. The functions framework includes the modules, e.g., intelligent sensing, intelligent simulation, intelligent diagnosis, intelligent early warning, and intelligent regulation, intelligent disposal, and intelligent control, et.al.

Assessing the Usefulness of Testing for Validating and Correcting Security Risk Models Based on Two Industrial Case Studies

The authors present the results of an evaluation in which the objective was to assess how useful testing is for validating and correcting security risk models. The evaluation is based on two industrial case studies. In the first case study the authors analyzed a multilingual financial Web application, while in the second case study they analyzed a mobile financial application. In both case studies, the testing yielded new information which was not found in the risk assessment phase. In particular, in the first case study, new vulnerabilities were found which resulted in an update of the likelihood values of threat scenarios and risks in the risk model. New vulnerabilities were also identified and added to the risk model in the second case study. These updates led to more accurate risk models, which indicate that the testing was indeed useful for validating and correcting the risk models.

A Multidimensional Approach towards a Quantitative Assessment of Security Threats

Information security is the most challenging aspect of information processing. Organizations, governments, and individuals are facing many information security risks. These risks can cause serious damages that might lead to significant financial losses, breach of the confidentiality of sensitive information, or loss of integrity or availability of sensitive data. To facilitate effective protection of information, a better identification, understanding, and assessment of security threat and their characteristics are crucial for system security managers. In order to define and then assess security threats, we propose a new threat identification approach on which we build a quantitative security risk model for information systems. The proposed model is systematic, extendable, and modular. The aim is to help managers accurately assess security threat in an incremental and comprehensive way.

Development of a cyber security risk model using Bayesian networks

Cyber security is an emerging safety issue in the nuclear industry, especially in the instrumentation and control (I&C) field. To address the cyber security issue systematically, a model that can be used for cyber security evaluation is required. In this work, a cyber security risk model based on a Bayesian network is suggested for evaluating cyber security for nuclear facilities in an integrated manner. The suggested model enables the evaluation of both the procedural and technical aspects of cyber security, which are related to compliance with regulatory guides and system architectures, respectively. The activity-quality analysis model was developed to evaluate how well people and/or organizations comply with the regulatory guidance associated with cyber security. The architecture analysis model was created to evaluate vulnerabilities and mitigation measures with respect to their effect on cyber security. The two models are integrated into a single model, which is called the cyber security risk model, so that cyber security can be evaluated from procedural and technical viewpoints at the same time. The model was applied to evaluate the cyber security risk of the reactor protection system (RPS) of a research reactor and to demonstrate its usefulness and feasibility.