Cybersecurity Vulnerabilities in Smart Grids with Solar Photovoltaic: A Threat Modelling and Risk Assessment Approach

Cybersecurity is a growing concern for smart grids, especially with the integration of solar photovoltaics (PVs). With the installation of more solar and the advancement of inverters, utilities are provided with real-time solar power generation and other information through various tools. However, these tools must be properly secured to prevent the grid from becoming more vulnerable to cyber-attacks. This study proposes a threat modeling and risk assessment approach tailored to smart grids incorporating solar PV systems. The approach involves identifying, assessing, and mitigating risks through threat modeling and risk assessment. A threat model is designed by adapting and applying general threat modeling steps to the context of smart grids with solar PV. The process involves the identification of device assets and access points within the smart grid infrastructure. Subsequently, the threats to these devices were classified utilizing the STRIDE model. To further prioritize the identified threat, the DREAD threat-risk ranking model is employed. The threat modeling stage reveals several high-risk threats to the smart grid infrastructure, including Information Disclosure, Elevation of Privilege, and Tampering. Targeted recommendations in the form of mitigation controls are formulated to secure the smart grid’s posture against these identified threats. The risk ratings provided in this study offer valuable insights into the cybersecurity risks associated with smart grids incorporating solar PV systems, while also providing practical guidance for risk mitigation. Tailored mitigation strategies are proposed to address these vulnerabilities. By taking proactive measures, energy sector stakeholders may strengthen the security of their smart grid infrastructure and protect critical operations from potential cyber threats

A Study on Security Attributes of Software-Defined Wide Area Network

For organizations to communicate important data across various branches, a reliable Wide Area Network (WAN) is important. With the increase of several factors such as usage of cloud services, WAN bandwidth demand, cost of leased lines, complexity in building/managing WAN and changing business needs led to need of next generation WAN. Software-defined wide area network (SD- WAN) is an emerging trend in today’s networking world as it simplifies management of network and provides seamless integration with the cloud. Compared to Multiprotocol Label Switching (MPLS) majorly used in traditional WAN architecture, SD-WAN incurs less cost, highly secure and offers great performance. This paper will mainly focus to investigate this next-generation WAN’s security attributes as security plays a crucial role in SD-WAN implementation. The goal of the paper is to analyze SD-WAN security by applying principles of CIA triad principle. Comparison of SD-WAN products offered by three different vendors in SD-WAN market with respect to its security is another important area that will be covered in this paper

Risk management and architecture design in securing cloud platforms: Case study of cloud models

Utilization of cloud environment has become more relevant for different companies and industries and should be considered when building new projects and migrating service from different service providers. As companies are trying to utilize cloud environments the knowledge about these might be lacking and thus increasing knowledge and introducing possible solutions is essential. This means that increasing knowledge about different approaches possible in cloud also different issues can be identified. Based on this kind of knowledge can the discussion about the possibility for utilizing cloud environments be improved. The use case for this study is the risk management and architecture design comparing of different cloud types and models based on a case study. Also, based on these different kinds of cloud types and models the security issues and countermeasures are discussed in a way that these measures could help to control or mitigate issues from happening. For finding feasible architecture designs these measures are to be considered alongside the responsibilities for different cloud models with the help of risk management. Risk management itself introduces risks and issues that are identified from cases and discussed as of how to control them within different cases. This thesis studies the possible issues and risks through a literature review that are associated with different cloud types and models. Also, introducing case study of three different cases that utilize these approaches and introduces such issues and risks associated with those cases. For identified issues and risks also relevant security methods and measures are studied through literature review and introduced to be utilized in risk management and architecture design. Based on these reviews a risk management is conducted to introduced cases where issues and risks are introduced with identification of real-world use case. Also, architecture design is introduced in a way that utilizes identified risks, control, and mitigation measures for protecting resources. What different possibilities and components to consider depending on different cases are also discussed as not all the risks can be mitigated with certain measures and would need more thought on as of what cloud type and model to utilize. Thesis also discusses about the three identified topics of risks, security measures and architecture and identifies relevant information from them for consideration. Thesis discusses about three different cases that were studied in a way as of how they differentiate from each other in the common field of risks, security measures and architecture design as they utilize the cloud in a different way. Discussion introduces the results and more detailed discussion that were identified from these three main topics. Detailed discussion itself contains similarities and differences identified from different cases and introduces more discussions based on those topics

Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

Cloud Computing cost and energy optimization through Federated Cloud SoS

2017 Fall.Includes bibliographical references.The two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by: (1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means. This work analyzes the core SoS requirements, concept synthesis, and functional architecture. It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks

Policy-driven Security Management for Gateway-Oriented Reconfigurable Ecosystems

abstract: With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices. This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications.Dissertation/ThesisMasters Thesis Computer Science 201

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems