Security Risk Assessments and Shortfalls for Evaluating and Protecting Dynamic Autonomic Systems in Future Internet

The increasingly complex power grid structure and the volatility caused by the high proportion of renewable energy have brought great challenges to the traditional static security assessment of power grids. To solve this problem, a multi-source data-driven power grid static security risk assessment method is proposed in this paper. Firstly, the historical operation data of the power grid, including the grid structure of the system, load power, generator power and weather information, are used to build a multi-source data set. Then, a static security assessment model is constructed using long-term and short-term memory neural network and deep neural networks, and the multi-source data sets are used for off-line training. According to the output results of the assessment model, a three-level static security risk assessment index system is then developed. Finally, the proposed method is tested through a provincial 500kV power grid. The example results show that the method proposed in this paper can effectively realize the assessment of power grid static security risks, which can be used to assist the system operators for future intelligent dispatch and control.

This chapter summarizes innovations in security risk and capability assessment frameworks and methodologies to directly support dynamic decision making, forge a convergence with S&T, operations, intelligence and policy, and inform investment priorities of the Canadian Safety and Security Program (CSSP). Using a thematic approach, this paper reviews how multiple innovations were adopted as “best practices” into the conceptual design, development and implementation of security risk assessment frameworks and methodologies in the CSSP. These include the: use taxonomies to foster key stakeholder ‘buy in’; leveraging architecture frameworks to drive tools, methods and rating schema; the formulation of scenario based approaches for assessing high consequence all hazards risks; and, the application of an integrated risk and capability gap assessment model to guide investment in public safety and security S&T. In the CSSP, the innovations in security risk assessment identified best practices and common tools that supported numerous strategies and S&T portfolios (e.g., CBRNE, Critical Infrastructure Protection, all hazards safety and security; defence infrastructure/military bases), where risk assessment expertise/capability improvements that were adopted by practitioners, interdisciplinary teams and decision makers. Underpinning these innovations were strong partnerships and community-based approaches that enabled a credible risk-informed decision support environment, with consistent/scalable approaches to analysis and decision making under conditions of uncertainty.KeywordsSecurity risk assessmentVulnerability

Critical systems have very stringent requirements on both security and safety. Recent mishaps such as the missing MH370 aircraft and the sunk Korean Sewol ferry go to show that our technology in safety and security risk assessment still need a more integrated approach. Nuclear plant meltdown in the recent Fukushima accident is also a typical example of insufficient risk assessments. This work is a case study on how a unified security and safety risk assessment methodology may be applied to a High Pressure Core Flooder (HPCF) system in a nuclear power plant. Individual risk security or safety assessments may overlook the possible higher risk associated with such critical systems. The case study shows how the proposed method provides a more accurate risk assessment compared to individual assessments.

Due to the lack of the support from the main grid, the islanded hybrid ac/dc microgrid will more likely cause the power flow severe fluctuation and even the microgrid to be split once a serious contingency occurs. To maintain the security and stability of the system under such a condition, this paper proposes a new static security risk assessment of the islanded hybrid ac/dc microgrid considering that the hybrid ac/dc microgrid was split. First, this paper presents a static security analysis criterion to determine the post-contingencies splitting scheme and a new load-shedding strategy to achieve power balance of subsystems. On this basis, a probabilistic power flow calculation method based on Levenberg-Marquardt with a nonmonotone line search (LMNL) algorithm is applied to calculate the probabilistic power flow of the islanded subsystems. Finally, the probability distribution of static security risk assessment indexes, including the dc loss load risk, ac loss load risk, and interlinking converter (ILC) active power limit violation risk, are obtained. The simulation results on the test system demonstrate the necessity of considering the hybrid ac/dc microgrid was split, and prove the feasibility and effectiveness of the proposed method.

Static risk factors have been viewed as relatively fixed aspects of offenders’ histories, such as age and the extent of previous offending, which increase the risk of reoffending but do not change and cannot be changed through intervention. Given that they are “fixed” historical characteristics, static risk factors are not typically seen as meaningful targets for intervention. In contrast, dynamic risk factors (DRFs) have been viewed as possible psychological or behavioral features of the offender that raise the risk of reoffending but maybe potentially changeable. The notion that some dynamic risk factors may be relatively stable for sexual offenders in that change occurs over extended periods of time, while other DRFs are less stable and may change acutely based on immediate contexts or affective experiences. Some authors have suggested that both proposed static and dynamic risk factors each are markers of underlying psychological vulnerabilities to commit sexual offenses and others have argued that such vulnerabilities or needs reflect propensities to act out sexually. In this chapter, two efforts to identify and quantify dynamic risk factors are reviewed that have culminated in two risk assessment instruments, the Stable-2007 and the SRA-FV. The results of the research efforts suggest that each of these instruments provides a useful measure of characteristics of sexual offenders that are related to sexual offense recidivism; they have been shown to have incremental validity relative to so-called static risk assessments. However, instead of being truly “dynamic” and changeable, such risk factors may simply reflect a more general or broader dispositional characterization of sexual offenders and one that surprisingly captures less variance than the more concrete manifestation of underlying psychological vulnerabilities as expressed in static, historical behaviors.

Sandia National Laboratories (SNL) has developed a number of security risk assessment methodologies (RAMs) for various infrastructures including dams, water systems, electrical transmission, chemical facilities and communities. All of these RAMs consider potential malevolent attacks from different threats, possible undesired events and consequences and determine potential adversary success. They focus on the assessment of these infrastructures to help identify security weaknesses and develop measures to help mitigate the consequences from possible adversary attacks. This paper focuses on RAM-C, the security risk assessment methodology for communities. There are many reasons for a community to conduct a security risk assessment. They include: providing a way to identify vulnerabilities, helping a community to be better prepared in the event of an adversary attack, providing justification for resources to address identified vulnerabilities and planning for future projects. The RAM-C process is a systematic, risk-based approach to assess vulnerabilities and make decisions based on risk. It has provided valuable information to community planners in making security risk decisions.

Due to the high probability of power flow violation during the N-1 static safety check under maintenance, and the risk assessment indicators are single and subjective, a method of static security risk assessment based on multi-source heterogeneous information for planned maintenance is proposed. By regularizing dispatcher experience, the static security factors are analyzed from equipment failure rate, electrical characteristics and grid topology, etc. Then, the selected indicators are preprocessed. Finally, Deep Belief Neural Network (DBNN) is used to evaluate risk. Through deep mining of the membership and mapping relationship between multiple indicators, the risk self-assessment is realized, and dispatcher is given the assistant decision when orderly adjusting equipment that causes limit. Simulation results of IEEE 39-bus show that the method proposed in this paper can quickly assess risk level without analysis of dispatcher; compared with DNN algorithm, DBNN has higher accuracy. The validity and feasibility of this research are proved.

The article provides a rationale for the practicality and features of using classic basic survival analysis methods for evaluating the effectiveness of financial monitoring and cyber security systems. The need to study this problem is due to the inability of modern systems of financial monitoring and cyber security at the state level to counteract various types of threats, especially in martial law conditions and the decline of economic growth. It is proposed to determine the effectiveness level of financial monitoring and cyber security systems using an integral indicator, the calculation of which is based on two composite indicators – the critical index of the cyber security system development level and the system development level essential index, which is countering the criminal proceeds legalization. Five qualitative levels of financial monitoring and cyber security systems effectiveness are distinguished. The survival function is interpreted in terms of the subject area. In terms of survival analysis, a level's change of the financial monitoring and cyber security systems integral indicator or the impossibility of its calculation due to the unavailability of statistical data is chosen as a critical event considered as the "death" of the system. The expediency of using three primary methods – the method of life table analysis, the Kaplan–Meier method and Cox regression – for evaluating the effectiveness of financial monitoring and cyber security systems is substantiated. The main arguments in favour of practicality were the possibility of using censored data, which is especially relevant in the absence of the necessary statistical information; the ability of survival analysis methods not only to study the effectiveness of financial monitoring and cyber security systems in time's periods but also to assess the factors that influence it; availability of separate survival analysis modules in data analysis computer programs.

School systems may pay attention to the fact that individuals and companies using smart devices are increasingly at risk of becoming victims of cybercrime. The literature on how effective students in developed countries such as the Netherlands are taught about cyber security skills during their school career is scarce. Although curriculum materials are available, scaling up computer science education is behind. Therefore, this study explores to what extent Dutch students develop cyber secure behavior at elementary and high school. A questionnaire was used for self-assessment of cyber security behavior. After the questionnaire was completed, two group interviews were conducted to improve the interpretation of the questionnaire results. The study findings revealed that the Dutch school curriculum hardly pays attention to this topic and that students acquire their online behavior mainly through experience, instructions on the internet, through parents, and through siblings. In addition, many students developed more reckless behavior over time. We recommend that cyber security education should start at elementary school as soon as children begin to use online equipment. A subject that deserves special attention is recognizing phishing emails and phishing websites. The learners should be convinced that risky behavior on the internet may turn against them and against the organization to which they belong.

The Internet of Things (IoTs) is apace growing, billions of IoT devices are connected to the Internet which communicate and exchange data among each other. Applications of IoT can be found in many fields of engineering and sciences such as healthcare, traffic, agriculture, oil and gas industries, and logistics. In logistics, the products which are to be transported may be sensitive and perishable, and require controlled environment. Most of the commercially available logistic containers are not integrated with IoT devices to provide controlled environment parameters inside the container and to transmit data to a remote server. This necessitates the need for designing and fabricating IoT based smart containers. Due to constrained nature of IoT devices, these are prone to different cyber security attacks such as Denial of Service (DoS), Man in Middle (MITM) and Replay. Therefore, designing efficient cyber security framework are required for smart container. The Datagram Transport Layer Security (DTLS) Protocol has emerged as the de facto standard for securing communication in IoT devices. However, it is unable to minimize cyber security attacks such as Denial of Service and Distributed Denial of Service (DDoS) during the handshake process. The main contribution of this paper is to design a cyber secure framework by implementing novel hybrid DTLS protocol in smart container which can efficiently minimize the effects of cyber attacks during handshake process. The performance of our proposed framework is evaluated in terms of energy efficiency, handshake time, throughput and packet delivery ratio. Moreover, the proposed framework is tested in IoT based smart containers. The proposed framework decreases handshake time more than 9% and saves 11% of energy efficiency for transmission in compare of the standard DTLS, while increases packet delivery ratio and throughput by 83% and 87% respectively.

Multi-modal cyber security based object detection by classification using deep learning and background suppression techniques

Since the latest downturn of the Oil and Gas (O&G) sector, productivity growth has become the priority, which led to the adoption of new key technologies for better performance and lower cost. As part of the 4th Industrial Revolution, these technologies are taking the O&G by storm, forcing hardware manufactures to innovate quickly and create devices with more capabilities. What was impossible a few short years ago is reality today. Managed pressure drilling (MPD) systems are taking advantage of this development by implementing advanced data analytics and hydraulic modeling in programable automation controllers. This approach has several benefits, reliability and full real-time deterministic operation being the major ones. Previously, controlling bottomhole pressure (BHP), the required modeling and analysis were executed in a networked computer at a different physical location. Now it is executed locally in a fully deterministic control system. This includes data acquisition (DAQ), signal conditioning, well bore modeling, analytics and data visualization. This approach mitigates the risk of connectivity issues and makes the system more robust in case of network failure. Furthermore, it simplifies the deployment of the system, as all critical functions are at a single physical location. Another major benefit is an increased resolution of the model in areas of specific interest. For example, a zoom in on the openhole section and a lower resolution in the cased hole section. The flexibility of this approach manifests itself on the deployment side. This system can be installed in several different scenarios, for example as a full system with controls and manifolds, or as a control system only, and integrated into existing rig MPD manifolds and drilling controls. This level of integration creates robust and flexible systems while reducing downtime during operations. This new approach includes the integration of cyber secure communications, an upcoming standard in the O&G industry. The system communicates via the open platform communications unified architecture (OPC UA) protocol – recommended by the Cyber Security community. This new platform integrates advanced data analytics and hydraulic modeling in real-time, coupled with cyber security features via OPC UA protocol directed to automation controllers for managed pressure drilling applications.

The smart grid concept is being fostered due to required evolution of the power network to incorporate distributed energy sources (DES), renewable energy sources (RES), and electric vehicles (EVs). The inclusion of these components on the smart grid requires an information and communication technology (ICT) layer in order to exchange information, control, and monitor the electrical components of the smart grid. The two-way communication flows brings cyber security issues to the smart grid. Different cyber security countermeasures need to be applied to the heterogeneous smart grid according to the computational resources availability, time communication constraints, and sensitive information data. This paper presents the main security issues and challenges of a cyber secure smart grid, whose main objectives are confidentiality, integrity, authorization, and authentication of the exchanged data.

The advent of quantum computing poses significant threats to traditional cybersecurity systems, which rely on cryptographic algorithms vulnerable to quantum attacks. This research explores the utilization of artificial intelligence (AI) in designing a prototype cybersecurity system aimed at countering the emerging threats of quantum computing. The study integrates AI-based intrusion detection systems with quantum-resistant cryptographic techniques to enhance the security of digital systems against quantum-driven cyberattacks. A combination of machine learning algorithms, such as neural networks and decision trees, is employed to detect and mitigate potential cybersecurity breaches. Additionally, post-quantum cryptography (PQC) is incorporated to safeguard sensitive data against the capabilities of quantum computers. The results indicate that the proposed system outperforms conventional cybersecurity methods in detecting threats with greater speed and accuracy, while also providing robust protection against quantum-enabled attacks. Moreover, the integration of AI allows for adaptive and proactive defense mechanisms, improving the overall system resilience. The study concludes that combining AI with post-quantum cryptography offers a promising solution to secure digital infrastructures in the face of quantum computing advancements, marking a significant step toward future-proofing cybersecurity strategies.

Cyber-Security in the Internet of Things (IoT) is a major concern for information exploitation which hinder the growth of information system. To address security levels and issues, security risk assessment is considered an effective tool for system security, products, process, and readiness. Effective system vulnerabilities guidance is involved in the prioritization of security risk assessment. At present, the differential equation provides a significant tool for risk assessment. However, for second-order derivatives, the error rate is higher which impacts on overall risk assessment model. To overcome those limitations, this paper presented Decision Support Light Weight Risk Assessment Model (DSLiRAM). The proposed DSLiRAM is the domain-specific framework for security assessment. The proposed DSLiRAM is adopted in four stages for the specification of practices applied for cybersecurity and organizational characteristics. The proposed DSLiRAM includes a fuzzy differential equation with a second-order derivative. To minimize error rate Taylor series expansion is integrated with Fredholm for risk assessment. The proposed DSLiRAM is examined in three scenarios, RT server, BPCS, and HMI. Analysis of results stated that the proposed DSLiRAM significantly predicts risk and prevents the attack.