Cyber Physical Energy Systems E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 863

Veröffentlichungsjahr: 2024

Beschreibung

This book is essential for understanding the transformative integration of cyber-physical systems in smart grids, providing valuable insights that will shape the future of sustainable energy production and distribution.

A novel modeling methodology that blends cyber and physical components is a significant advancement for future energy systems. A Cyber-Physical System (CPS) is an integrated component of physical microgrids that combines computers, wireless connections, and controls to create a holistic solution. As a result of cyber-physical systems, a new generation of engineering systems incorporating wireless communication has begun to emerge. Despite that there are various major CPS systems in use today, one of the most challenging sectors for implementation is the smart grid which aims to distribute dependable and efficient electric energy while maintaining a high level of global environmental sustainability.

Smart grids incorporate advanced monitoring to ensure a secure, efficient energy supply, enhancing generator and distributor performance while offering consumers more choices. These systems aim to boost the capacity and responsiveness of energy production, transmission, distribution, and consumption. As renewable energy sources grow, traditional methods are being challenged, requiring cross-domain integration of energy systems and data. This book explores architectures and methods for integrating cutting-edge technology into the power grid for more sustainable energy production and distribution.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Cyber-Physical Systems: A Control and Energy Approach

1.1 Introduction

1.2 Studies on CPES Safety

1.3 Threat Evaluation

1.4 Theory of Cyber-Physical Systems Risk

1.5 Threat Evaluation Methodology

1.6 Experimental Setup for Cross-Layer Firmware Threats

1.7 Conclusion

References

2 Optimization Techniques for Energy Management in Microgrid

2.1 Introduction

2.2 Explanation Methods for EMS

2.3 EQN EMS on an Arithmetic Optimization Basis

2.4 Heuristic-Oriented Methods to EMS Problem-Solving

2.5 EMS Solution Techniques Using Meta-Heuristics

2.6 Alternative EMS Implementation Strategies

2.7 Conclusion and Viewpoints

References

3 Cyber-Physical Energy Systems for Smart Grid: Reliable Distribution

3.1 Introduction

3.2 Cyber-Physical Energy Systems (CPES)

3.3 Forming Energy Systems

3.4 Energy Efficiency

3.5 Smart Grids

3.6 Cyber-Physical Systems

3.7 SG: A CPS Viewpoint

3.8 Upcoming Prospects and Contests

3.9 Conclusion

References

4 Evolution of AI in CPS: Enhancing Technical Capabilities and Human Interactions

4.1 Introduction to Cyber-Physical System

4.2 The Cyber-Physical Systems Architecture

4.3 Cyber-Physical Systems as Real-Time Applications

4.4 Impact of AI on Cyber-Physical Systems

4.5 Policies

4.6 Expected Benefits and Core Promises

4.7 Unintended Consequences and Implications for Policy

4.8 Employment and Delegation of Tasks

4.9 Safety, Responsibility, and Liability

4.10 Privacy Concerns

4.11 Social Relations

4.12 Economic Study on CPS

4.13 Case Studies

4.14 Conclusion

References

5 IoT Technology Enables Sophisticated Energy Management in Smart Factory

5.1 Introduction

5.2 IOT Overview

5.3 IOT Enabling Technology

5.4 IOT in Energy Sector

5.5 Challenges of Applying IOT

5.6 Reference Architecture for IoT-Based Smart Factory

5.7 Characteristics of Smart Factory

5.8 Challenges for IoT-Based Smart Industry

5.9 How IoT Will Support Energy Management in Smart Factory

5.10 IoT Energy Management Architecture for Industrial Applications

5.11 Case Study: Smart Factory

5.12 Conclusion

References

6 IOT-Based Advanced Energy Management in Smart Factories

6.1 Introduction

6.2 Smart Factory Benefits of IOT-Based Advanced Energy Management

6.3 Role of IOT Technology in Energy Management

6.4 Developing an IOT Information Model for Energy Efficiency

6.5 Integrating Intelligent Energy Systems (IES) and Demand Response (DR)

6.6 How to Accurately Measure and Manage Your Energy Usage

6.7 Introduction to Energy Efficiency Measures

6.8 Identifying Opportunities to Reduce Energy Use

6.9 Monitoring and Measuring Energy Usage

6.10 Establishing Accounting and Incentives

6.11 Sustaining the Long-Term Benefits of Optimized Energy Usage

6.12 Role of Cyber Security When Implementing IoT-Based Advanced Energy Solutions

6.13 Materials Required in Smart Factories

6.14 Methods in IoT-Based Smart Factory Implementation

6.15 Steps for Developing an IoT-Based Energy Management System

6.16 Challenges For Adopting IoT-Based Energy Management Systems

6.17 Recommendations for Overcoming the Challenges With Implementing IoT-Based Advanced Energy Solution

6.18 Case Studies

6.19 Case Studies for Successful Implementation

6.20 Applications

6.21 Different Techniques for Monitoring and Control of IoT Devices

6.22 Literature Survey

6.23 Conclusion

References

7 Challenges in Ensuring Security for Smart Energy Management Systems Based on CPS

7.1 Introduction

7.2 Cyber-Physical Systems and Smart Energy Management

7.3 Security Challenges in CPS-Based Smart Energy Management

7.4 Cyber Security Standards and Guidelines for Smart Energy Management

7.5 Conclusion

References

8 Security Challenges in CPS-Based Smart Energy Management

8.1 Introduction

8.2 CPS Architecture

8.3 The Driving Forces for CPS

8.4 Advances in Cyber-Physical Systems

8.5 Energy Management through CPS

8.6 Security Issues in CPS

8.7 Open Challenges and Future Directions

8.8 Conclusion

References

9 Blockchain-Based Energy Transmission System: Design, Optimization, and Data-Driven Modeling

9.1 Introduction

9.2 Literature Review

9.3 Case Study and Application

9.4 Conclusion

References

10 Explainable AI Technology in E-CPS: Policy Design, Economic Research, and Case Studies

10.1 Introduction

10.2 E-CPS Arrangement

10.3 Case Study: Method Depiction

10.4 Transformation of the Power Infrastructure

10.5 Power Managing Structures

10.6 Protection Policies

10.7 Urgent Need for Effective Governance of AI and e-CPS

10.8 Conclusions

References

11 Infrastructural Data Visualization and Improved User Interfaces of Energy Consumption in Smart Cities

11.1 Introduction

11.2 Literature Review

11.3 Visualization Tools and Interfaces Used in Smart Cities Using IoT

11.4 Energy Representation Frameworks

11.5 Materials and Methods

11.6 Case Study and Applications

11.7 Factors for the Improvement of Energy Efficacy in Smart Cites

11.8 Conclusion and Future Scope

References

12 Power Management in Intelligent Buildings Based on Daily Demand Prediction

12.1 Introduction

12.2 The Power Management System Block Diagram

12.3 Working Task of Power Management System

12.4 Simulation Model of Power Management System

12.5 Hardware Implementation of Power Management System

12.6 Safety Precautions in Smart Building Implementation

12.7 Conclusion

References

13 Schemes and Security Attacks on the Integrity of Cyber-Physical Systems in Energy Systems

13.1 Introduction

13.2 CPS Safety Methodologies

13.3 Shielding in Contradiction of Information Safety Assaults

13.4 Scheme Variants

13.5 Supervised Learning in Depth

13.6 Discussion

13.7 Conclusion

References

14 Adaptive Power System Resource Management in Cyber-Physical Energy Systems

14.1 Introduction

14.2 CPES Structures

14.3 Development of CPES Structures

14.4 Resource Management in Socio-CPS

14.5 Associated Study

14.6 The Integrated Modelling Platform for the CPES

14.7 Assault from Without and Compounding Collapse

14.8 The Combination of Assault and Defence

14.9 Case Studies

14.10 Conclusions and Future Work

References

15 Cyber-Physical Energy Systems for Electric Vehicles

15.1 Introduction

15.2 Suggested Type

15.3 Outcomes from Experiments and Simulations

15.4 Discussion

15.5 Conclusion

References

16 Design and Implementation of IoT-Based Advanced Energy Management System for Smart Factory

16.1 Introduction

16.2 Challenges Faced by Factories Today

16.3 Home Energy Management Systems (HEMSs)

16.4 Micro Grid for Integration of Several Sources and Storage

16.5 Proposed Robust Energy Management System for Smart Factories

16.6 Conclusion

16.7 Future Trends

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Architectures of cyber-physical testbeds as well as information on t...

Table 1.2 Operational tiers, system types, and related parts of the ICS.

Table 1.3 Sample of calculating strike impact.

Table 1.4 Quality measures for the physical system. The indicators are separat...

Chapter 2

Table 2.1 Merits and demerits of EMS.

Table 2.2 Proportional research of ML models [18].

Chapter 4

Table 4.1 Comparison of the different categories of unintended consequences an...

Table 4.2 Comparison of different case studies in the aspect of their advantag...

Chapter 5

Table 5.1 Challenges and present-day resolutions for utilizing IoT in the ener...

Chapter 7

Table 7.1 CPS security threats in the smart grid and industrial control system...

Chapter 9

Table 9.1 PoW hash function energy consumption comparison [43, 44].

Chapter 10

Table 10.1 E-CPS modulation technique.

Table 10.2 Outcomes of dual power structures modelling.

Table 10.3 Lists a few uses of cyber-physical technology in the power infrastr...

Table 10.4 A few ML techniques mentioned in the field on power systems.

Chapter 11

Table 11.1 Context of ML algorithms in smart cities.

Chapter 13

Table 13.1 Representations.

Chapter 14

Table 14.1 Resource depletion in various assault situations, presuming no assa...

Table 14.2 Resources used by the defence in Scenario 3.

Table 14.3 Anticipated load reduction post protective resource deployments.

Chapter 15

Table 15.1 Electrical motors and conversion parameters.

Table 15.2 Transferring energy among various sources of power.

Table 15.3 Evaluation of alternative EV-controlling methods.

List of Illustrations

Chapter 1

Figure 1.1 Cyber-physical test chamber constituents for the EPS study.

Figure 1.2 The elements of the comprehensive vulnerability architectures are t...

Figure 1.3 Outline of the operational threat categories and capabilities for C...

Figure 1.4 Method of simulating a conceptualized illustration of networking co...

Figure 1.5 Representation of power systems used in firmware conceptually, with...

Figure 1.6 Assault on many layers of software that targets the maximum power p...

Figure 1.7 Luminous flux profiles, DC voltage value, and DC energy density are...

Chapter 2

Figure 2.1 Model of a “microgrid”.

Figure 2.2 Categories of undefined constraints.

Figure 2.3 Unpredictability control approaches.

Figure 2.4 Price structure and supply planning.

Figure 2.5 How to solve an issue with EM.

Figure 2.6 How optimization techniques were used for the EM.

Chapter 3

Figure 3.1 Typical designs and their targeted investigations.

Figure 3.2 Schematic diagram of a smart grid.

Figure 3.3 CPS structure.

Figure 3.4 SG from a CPS view.

Figure 3.5 Power network.

Chapter 4

Figure 4.1 Framework of CPS.

Figure 4.2 5C architecture or CPS.

Figure 4.3 Application of cyber-physical system.

Figure 4.4 Cyber and physical components of CPS.

Figure 4.5 CPS in healthcare.

Chapter 5

Figure 5.1 Supply chain of energy [10].

Figure 5.2 Cyber-physical system component [12].

Figure 5.3 Cyber-physical systems [12].

Figure 5.4 Component of an IoT platform [10].

Figure 5.5 The evolution of the Internet [20].

Figure 5.6 Data link protocol [20].

Figure 5.7 IoT protocol [20].

Figure 5.8 IOT-enabled technologies [45].

Figure 5.9 IoT in the energy sector [44].

Figure 5.10 Application of IoT in the energy sector—regulation, market, and en...

Figure 5.11 Applications of IoT in the energy sector (2): energy grids and dem...

Figure 5.12 Reference architecture for IoT-based smart factory [5].

Figure 5.13 Energy saving and harvesting model [44].

Figure 5.14 IoT-based wireless network [44].

Figure 5.15 IoT-based smart industry construction [44].

Chapter 6

Figure 6.1 Block diagram of Energy storage and data analysis.

Chapter 7

Figure 7.1 Parts of a cyber-physical system [1].

Figure 7.2 Steps for the CPS design process [1].

Figure 7.3 CPS framework [1].

Figure 7.4 CPS protocol overview [8, 9].

Figure 7.5 The CPS’s sources of threats and the places they infiltrate [13].

Figure 7.6 Cyber security concepts for smart energy systems [17].

Figure 7.7 Cyber security standards and best practice guideline [17].

Chapter 8

Figure 8.1 CPS 5C architecture [11].

Figure 8.2 CPS 8C architecture [12].

Figure 8.3 Reference architectural model Industry 4.0 (RAMI 4.0) [13].

Figure 8.4 Application domains of CPSs.

Figure 8.5 Big data processing of MCPS.

Figure 8.6 CPS-based energy management system.

Figure 8.7 Optimal energy management of AEV [38].

Figure 8.8 Smart meter communication in home automation.

Chapter 9

Figure 9.1 A simplified blockchain with N blocks.

Figure 9.2 Evolution of blockchain.

Figure 9.3 Blockchain-based perception layer.

Figure 9.4 Blockchain-based control layer.

Figure 9.5 SDN-based IoT network [24].

Figure 9.6 Blockchain-based cloud layer.

Figure 9.7 Big data and blockchain assistance.

Figure 9.8 Different layers of smart city.

Figure 9.9 Blockchain architecture for MG [62].

Figure 9.10 Blockchain application based on smart grid optimization [25].

Figure 9.11 Application of Blockchain in various domains [26, 27].

Figure 9.12 Energy consumption by resource-constraint device [30].

Figure 9.13 Comparison among different consensus algorithms [32].

Chapter 10

Figure 10.1 E-CPS arrangement.

Figure 10.2 Procedure to establish E-CPS [9].

Figure 10.3 Architecture of a domestic facility’s sustainable power structure.

Figure 10.4 Deep learning-based autonomous position and capacity estimation of...

Figure 10.5 Construction of smart cyber-physical system.

Chapter 11

Figure 11.1 Smart city major components [3].

Figure 11.2 Big data.

Figure 11.3 Leading diseases in the literature of AI [21].

Figure 11.4 The key elements to be the future smart cities.

Figure 11.5 Energy efficiency in smart cities.

Figure 11.6 Sources of energy.

Figure 11.7 Various applications of smart cities using IoT.

Figure 11.8 Components of energy management.

Figure 11.9 Integration of smart grid.

Figure 11.10 GUI for smart transportation in smart cities.

Figure 11.11 Machine learning model.

Figure 11.12 Comparison of energy.

Chapter 12

Figure 12.1 The power management system block diagram.

Figure 12.2 Symbol and its resistance with a light intensity of LDR sensor.

Figure 12.3 Relay circuit and its circuit design.

Figure 12.4 Working module of the power management system.

Figure 12.5 Simulation circuit model using PROTEUS 7.0.

Figure 12.6 When all lamps (loads) in on condition.

Figure 12.7 When lamp 1 (loads) in on condition and other lamps in off conditi...

Figure 12.8 When lamp 1 and lamp 2 in on condition and lamp 3 in off condition...

Figure 12.9 When all lamps (loads) in off condition.

Figure 12.10 Hardware model of the energy management system.

Chapter 13

Figure 13.1 Structure of CPS safety.

Figure 13.2 Goals of CPS protection.

Figure 13.3 Strategies to CPS information safety.

Figure 13.4 IEEE 30 bus concepts.

Figure 13.5 (a) Intermediate assault. (b) Continual assault.

Chapter 14

Figure 14.1 CPES structures.

Figure 14.2 Schematic of the autonomously controlled console.

Figure 14.3 CPES structural design.

Figure 14.4 A cyber-physical energy systems’ nodes connection architecture (CP...

Figure 14.5 Nodes–linkage affiliations in a CPES.

Figure 14.6 CPES susceptibility evaluation process schematic drawing.

Figure 14.7 Altered IEEE 14 - bus network.

Chapter 15

Figure 15.1 Cyber-physical energy system structures [2].

Figure 15.2 Evaluation of various sources of power for electrical vehicles [6]...

Figure 15.3 Indicates the suggested configuration of SCPES for use with e-vehi...

Figure 15.4 Smarter CPE-transmitting system paradigm [9].

Figure 15.5 A multiple form of power network controlled by SOS.

Figure 15.6 Layout of a cyber-physical blended energy system [11].

Figure 15.7 Grouping of power-controlling techniques.

Figure 15.8 A proposed algorithmic control framework for an e-vehicle.

Chapter 16

Figure 16.1 IoT-enabled system [16].

Figure 16.2 Source: IEEE Standard 2030-2011.

Figure 16.3 Monitoring and control in active distribution network [9, 15].

Figure 16.4 Components of energy management information systems.

Figure 16.5 Essential sensing technologies in smart factories [10, 11].

Figure 16.6 Smart energy system architecture [23].

Figure 16.7 Proposed robust energy management system for smart factories.

Figure 16.8 Smart energy management configuration for a smart factory.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Cyber Physical Energy Systems

Edited by

Shrddha Sagar

T. Poongodi

Rajesh Kumar Dhanaraj

and

Sanjeevikumar Padmanaban

This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-17252-8

Front cover images supplied by Pixabay.comCover design by Russell Richardson

Preface

The rapid evolution of technology has steered in an era where the integration of cyber-physical systems (CPS) with energy management is redefining how we approach energy consumption and distribution. As cities grow smarter and industries become increasingly interconnected, the need for efficient, reliable, and secure energy systems has never been more critical. This book explores the multifaceted landscape of energy management in cyber-physical environments, focusing on the interplay between control systems, smart grids, and the Internet of Things (IoT). The rise of explainable AI technology further enhances these systems by providing transparency in decision-making processes, making it easier for stakeholders to understand and trust AI-driven recommendations. Through a comprehensive analysis of these topics, we aim to provide readers with a deeper understanding of how cyber-physical systems can transform energy management practices. From the implementation of adaptive power system resource management to the exploration of user interfaces in smart cities, our goal is to highlight the innovative approaches shaping the future of energy consumption. In summary, this book serves as a guide for researchers, practitioners, and policymakers eager to navigate the complexities of energy management in cyber-physical systems. By embracing the synergy between technology and energy, we can forge a sustainable future that prioritizes efficiency, reliability, and security.

1Cyber-Physical Systems: A Control and Energy Approach

Shaik Mahaboob Basha1, Gajanan Shankarrao Patange2, V. Arulkumar3*, J. V. N. Ramesh4 and A. V. Prabu5

1Electronics and Communication Engineering, N.B.K.R. Institute of Science and Technology, Vidyanagar, Tirupati, Andhra Pradesh, India

2Mechanical Engineering, CSPIT–Chrusat, Charotar University of Science and Technology, Charusat Campus Changa, Anand, Gujarat, India

3School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

4Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

5Department of ECE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

Abstract

Cyber-physical systems (CPS) combine analogue and digital components to interact with the real world and are crucial to business and industry, including infrastructure like energy systems. Due to their critical nature, CPS is vulnerable to cyber-attacks, particularly phishing software that can impair their functionality. Attacks on CPS, especially on mission-critical components like energy distribution networks, can have severe consequences. To improve CPS protection, a technology demonstrator can replicate CPS behavior and identify vulnerabilities and protection mechanisms. A scenario modeling technique can accurately depict CPS components, relationships, attackers, access points, and network attacks. Risk modeling can outline the necessary resources to replicate CPS and generate large representations to assess network efficiency. The methodology includes evaluating the network using specific indicators, prioritizing cyber-attack prevention based on their impact on system function, and analyzing and preventing attacks using four example patterns that targeted CPES. This article aims to provide a staged process for conducting in-depth security evaluations that result in a safer and more durable CPS.

Keywords: Cyber-physical systems, energy systems, technology, CPES, network’s efficiency and risk analysis

1.1 Introduction

1.1.1 Background and Motivation

Energy systems have transitioned over the last few years from a single-directional production and dissemination system to an amplified distributed structure that supports both conventional sources of energy and distributed generation in the form of centralized generation, like wind and solar power, and distributed storage, like energy storage devices and energy storage systems by thermal means. The advancement of communications and information technologies, electronic control networks, environment monitoring, and integrated industrialized IoT technologies has largely made it possible for EPS to be transformed into CPES. The National Institute of Standards and Technology recognizes “designs that include electronic, analog, and hardware elements.” The characteristics of the network and the rules that govern its functioning define these parameters. By smoothly merging material objects with social, electronic, and connectivity elements meant to function via integrative physics and analytical reasoning, CPES are powerful complex systems revolutionizing the way conventional EPS functions. As a result, CPES contributes significantly to the transformation of EPS by enabling effective organization, more adaptable oversight, cyber-secure operational processes, framework efficiency, reconfigurable power generation (TES), and advancements in voltage stability, reliability enhancements, toughness, interconnectivity, and relatively clean energy production. Controlling and retaining protected access to critical framework resources and functions (for CPES: gen console deposits, recurrence consistency restrictions, power cable safeguards, and so on) as well as maintaining the confidentiality, ease of access, and truthfulness of the information being presented (for example, regulating the sequence of oversight monitoring and data procurement) pose significant challenges to CPES stability. As a huge development network of systems, CPS uses a variety of computer elements, including smart electronic devices, programmable controllers, and remote terminal modules, many of which were not created with safety in mind. Such gadgets’ architecture, firmware, and networking technology are often created using commercially available parts. As a result, flaws in such elements may be transferred to the CPS environment, potentially opening the door for nefarious adversaries seeking to disrupt CPS operations. In April 2019, a notification of a suspicious occurrence involving hostile conduct directed toward CPS operations was made.

The assailants used a recognized CPES weakness, specifically a web application firewall gap, to access one of the developed countries’ grid structures and launch a cognitive dissonance assault. The assault led to a communication issue here between the system for energy management and the facility’s generating units, which briefly disrupted operations. There is an increase in unauthorized access via hacking, with attackers exploiting current and reported flaws to breach CPS. In 2020, “98% of the holes accessed are known to safety specialists, while not a day’s worth of faults constitute just 0.5% of the responsibility exposed throughout the last decades,” according to international security. This statistic provides proof of this. The assailants may be persuaded to violate these networks in order to gain monetary or political gain because of the significance of CPS and CPES, specifically for productivity expansion and population health at the global, regional, and micro levels.

Therefore, it is crucial to assess the CPES’ stability and resistance to assaults in actual settings. In addition, since EPS—also known as the “biggest networked mechanism on the ground” [1]—integrates the impact of cyber across all sectors and sizes, the assessment of cyber threats becomes increasingly complicated and difficult. Sincerely, EPS activities might be understood by simulating certain unusual activities (such as failures, unbalanced voltage situations, frequency variations, etc.). To capture the nonlinear response of these standardization processes, increasingly precise descriptions and depictions are needed given the recent advancements toward smart and linked CPES. The improvement of CPES integrity and dependability necessitates the ongoing exploration of possible vulnerabilities [2]. The concept of security must take into account the CPES structure’s characteristics in extensive testing settings that permit the interface of hardware components that are intended to function in the “actual” network. Equipment (HIL) hardware platforms are useful in this situation because they provide testing procedures for determining how well physical and digital components are working together in limited circumstances.

In order to conduct cyber resilience and assess the consequences, recognize security weaknesses across numerous levels (e.g., memory modules, system software, applications, procedures, and methods), incorporate detection mechanisms and preventative measures algorithms, and evaluate the effectiveness of countermeasures without posing an undue financial burden or safety risks, protection HIL configurations are essential [3]. This article’s main goal is to provide a methodology that integrates conceptual and framework protection research studies, assessing CPS system behavior using testing ground settings and ultimately resulting in much more secure CPES designs. Assessment and experimentation research projects must be characterized and modeled, taking into account both the virtual and physical domains, in order to enable functional prototypes to accurately represent the features of the malware context. The research papers must provide thorough explanations of the tools and indicators that will be used to assess the effectiveness, dependability, and durability of the CPES. The evaluation configuration should also record the opponent’s vulnerability assessment attributes and the assault strategy. Threat modeling attributes for a possible enemy include antagonistic information, finances, the system’s access, and precision. Risk evaluation features for the attacking approach comprise offensive incidence, repeatability, and search capabilities, points in different targeted resources, attacker tactics, and foundation. Experts and interested parties may completely evaluate and identify potential threats present in the CPES under assessment by performing this task in a comprehensive and methodical manner.

1.1.2 Testbeds, Revisions, and a Safety Study for Cyber-Physical Energy Systems

This section describes the many CPES test chambers created by various research organizations and lists the tools used to carry out their research purposes.

We outline various types of CPES development studies seen in the field and discuss well-known examples from each. Additionally, we examine how vulnerability definition, prevention, and mitigation approaches may assist vulnerability analyses by identifying, avoiding, and reducing threats.

1.1.3 CPES Test Chamber

EPS have been built and modeled over the years using transversal topologies in which electricity is generated at massive mass energy plants and then transferred to users via various transmitting and circular delivery networks. The integration of renewable energies (RES) with distributed generation resources (DERs) required little effort [4]. Nevertheless, as RES and DER adoption rise and the grid is modernized using ICT, the intricacy of EPS also increases. In contrast, RES and DERs can be applied to supply dependable, reasonably priced, and environmentally friendly power to meet client demands. On the other hand, hackers may covertly implant their assaults on weak systems and equipment by making use of the fact that these capabilities are not generally regulated and are instead ultimately controlled by providers [5, 6]. Due to the complexity of the current EPS and the reliance of all these systems on ICT for inter-system interaction, there are several potential points of assault. Even if there is a clear need for safe and robust EPS, the problem is made worse by our lack of expertise working with and organizing such complex infrastructures. We lack the tools necessary to identify and lessen the effects of unforeseen unfavorable occurrences on the functioning of the power grid. The organization’s reliance on CPES interconnectedness, the layout of its electricity monitoring system, regulation, and prediction techniques, which are highly secure, heavily depends on the presence of reflective structures where future security features and methods can be evolved and analyzed. In-depth system assessments may be carried out in a perfect setting on CPES functional prototypes without affecting the true power system. When transferring particular processes to the real system, test chambers are used to minimize the risk and eliminate any possible negative effects. The verification and influence assessment of new EPS hardware (such as the assimilation of PV centres, infrastructure for EV recharging, etc.), updated tactics (such as the prioritization of electricity conveyance among RES, DER, or other sources of energy generation), as well as remediation techniques for unforeseen events (such as flaws, mechanical failures, cyber threats, etc.) are a few examples of these processes. Figure 1.1 shows the primary design components of such computer network testbeds. The following is a list of potential protection activities that might be carried out on CPES testbeds:

Teach partners and clients in a replicated or modeled CPES scenario.

Assess the functionality of process standardization comprehensively, that is, from the smallest operating levels (such as sensors, controllers, processes, etc.) to the top levels, such as remote monitoring and control.

Create and evaluate cyber-physical measurements and assess the security of the system.

Test new security technologies, including data encryption, access control, and systems that detect and prevent intrusions (IDS/IPS).

Assess the effect of assaults on the EPS’s physical and virtual realms.

Figure 1.1 Cyber-physical test chamber constituents for the EPS study.

Analyze the success of preventive tactics for negative cyber-physical occurrences.

1.1.4 Significance and Contributions of Testbed

Numerous institutions and established labs have created internal testbeds for study as well as for education and instruction due to the significance of vulnerability investigations for CPS and vital CPES facilities [7]. Various testbeds have been created and put into use based upon the request area with study purpose. The true modeling CPS testbeds that are now accessible are listed in Table 1.1 besides their unique capabilities. Our classification of technological testbeds takes into account factors such as structure, cost, and dependability. We also provide a thorough breakdown of the variations among intrusion detection and prevention and software-aided test environments. Equipment-oriented trial runs are intended to formally examine CPS and often include a number of real-world physical elements— for instance, CPES equipment-oriented testbeds include actual hardware like generators, switches, switchgear, ESS, photovoltaic systems, wind generators, etc. These testbeds enable contributors to (i) make decisions based on pragmatic experiments rather than theoretical assessments, (ii) analyze CPS behavior under abnormal conditions to demonstrate the potential without interfering with the proper machine’s procedure, and (iii) predict cyberattacks or malfunction remediation and statistically control. However, device-oriented testbeds have three major drawbacks, namely: (i) they are not inexpensive because the testbed elements must match the actual hardware used on the ground, (ii) after the device and testbed setups are set up, any change or augmentation of the network infrastructure can either take a significant amount of time or be virtually and financially impractical, and (iii) there are extensibility issues of great vertical EPS because of the testbed. The US Department of Energy’s Idaho National Laboratory (INL) is a model of a hardware-aided analysis facility that does CPES vulnerability scanning using actual, functional hardware [8]. The Performance and Energy Real-Time Laboratories, Nuclear Laboratory, and Micro Grid (MG) Testbed at INL enable the modeling of real-world scenarios using authentic tools and information-generating processes.

Table 1.1 Architectures of cyber-physical testbeds as well as information on their perfection, repetition, price, and samples of evaluation chamber by means of replication basis.

Test chamber structure

Accuracy

Repeatability

Cost

Example test chamber

Equipment support

High

Low

High

INL & NREL

Computer supported

Intermediate

High

Low

A&M and Dortmund

Composite: simulated computing and physical equipment

Intermediate

Intermediate

Intermediate

FSU-CAPS & PNNL

Composite: physical computing and simulated equipment

Intermediate

Intermediate

Low

HELICS

Investigators can construct complex situations by factoring in hardware components that are hooked up with genuine network simulators that utilize HIL strategies like console equipment as well as energy equipment, thanks to the genuine computation functionality of INL’s testbeds. Until it is fully integrated into the main grid, HIL enables comprehensive testing of regulators (CHIL) and components of EPS (PHIL). Equipment-oriented testbeds are also a part of the DOE’s National Renewable Energy Laboratory (NREL). The Flatirons campus of NREL is an expert in developing precise computational methods for hydroelectric power generating facilities, hydroelectricity, and wind generators. Their distinctive resources support the development of their elevated modeling techniques, which are related to tangible assets and serve as important tools for grid operators doing system studies that include dispersed hydroelectric, wind-generated energy, or off-shore production [9]. With the least amount of expense and, most notably, without jeopardizing the actual EPS operation, it is possible to examine the potential effects of equipment breakdown or computer security occurrences using the real power applications and operating systems of wind generators and hydroelectric plants as well as their modeling techniques. CPES testbeds with hardware assistance do not always make use of hardware compatibility. Most of the time, simulation technology that enables the study of more complicated systems is used to assist the study that is being done. Since it is often impossible to replicate an operating CPS exactly in the lab, several application-oriented CPS testbeds have been created in recent years in accordance with the idea of computer data. The fundamental distinction between equipment testbeds and their application equivalents is that the former lacks real field apparatus, which restricts the test cases that may be used. Furthermore, depending on the simulation environment used for the systems engineering, application testbeds may be further divided into subcategories. Genuine simulations (RTS) like Opal-RT, RTDS, Twister, and Speedgoat are used by some of them, while others depend on common software simulators like Matlab/Simulink, PowerWorld, PSSE, etc. Comparing application-oriented CPS testbeds to equipment-oriented testbeds, the versatility in developing, altering, and growing the devices under test is the key benefit. Additionally, their price may be much lower when replicating massive CPES. The veracity of the application-oriented replicated result, on the other hand, depends on the correctness of the models (used for emulating, hypervisor, etc.) used to stand in for the actual measured devices in the study.

1.1.5 Testbed Setup

Two testbed setups with extensive CPS modeling capabilities are those of Texas A&M and TU Dortmund. Even without real EPS hardware, at the Texas A&M CPS testbed, application solutions may be used to replicate and test CPES developments like the intelligent grid controller with RES. The testbed also allows the simulation of interactions between CPES elements using networking simulators and RTS systems (RTDS) (OPNET). It also allows researchers to assess how information exchange technology raises the risk surface. Huge application network such as SDN testbeds are being designed and developed as a result of the quick uptake of ICT tools in CPS. Experts may assess cutting-edge communications technology, interoperability, unique data scheduling algorithms, etc., in such SDN-type testbeds. The SDN4 SG CPS evaluation room at TU Dortmund, where ICT-based smart grid applications are being evaluated utilizing both SDNs and power system RTS, serves as one example of such a setup [10].

The TU Dortmund testbed includes the RTS (Opal-RT), which simulates the components of the energy system. SDN and the OPNET network computer simulation are used to enforce the architecture by simulating the network architecture and connectivity between both. They are designed to simulate grid assets (such as EVs, ESS, etc.), control system, as well as sensor data unit such as measurement devices (PMUs), the framework for improved meters CPS testbed methods with equipment and technology assistance. Alternative methods, as their name implies, compromise the use of hardware components that originate at the distribution and delivery (T&D) stage of CPES by utilizing software platforms in addition to simulations considered to perfectly mimic the action of actual world power method. Testbeds for hybrids allow a wide range of safety investigations as to can accentuate the cyber system (e.g., SCADA correspondence, transponders, and navigation system of investments, capable of monitoring and quantifying components, etc.), the physical structure (e.g., computer-controlled controller, grid assets, IEDs), or among the two groups. The fundamental benefit of such testbeds is that they provide customizable frameworks so as to may scale up via modeling to the sizes of genuine systems as still keeping the capacity to explore, at a high resolution, any security and management properties found in hardware objects. As a result, composite CPS testbeds lack any of the constraints associated with hardware- or software-assisted testbeds and may examine the effect of cyber-attacks on CPES comprehensively.

1.1.6 Illustration of Hybrid CPES Testbed Structure

HELICS is an excellent illustration of hybrid CPES testbed architecture. The HELICS architecture makes it possible to link T&D system parts and integrate several RTS that operate at various time steps. Cybersecurity evaluations involving real-time effect investigation and risk reduction methods can be performed by rapidly modeling (based on chronological constraints) complex T&D systems, providing insightful information about CPES behavior. The hybridized testbed at the Pacific Northwest National Laboratory (PNNL) makes use of the benefits outlined above. The testbed supports a range of cybersecurity investigations [11] and offers a comprehensive basis for system risk evaluation, dynamic CPES scenario models, attack scenario analyses, and harm minimization plan analyses. A combination testbed configuration is also part of the equipment of the Center for Enhanced Energy Network at State’s University. The testbed allows the usage of the RTS power system, depending on the Opal RT and RTDS platforms.

Open components for DSS, PSCAD/EMTDC, Matlab/Simulink, RT-Lab, RSCAD/RTDS physical, and EPS, together with gen, electrical converter, and stretchy AC distribution system, are examples of simulation software. The infrastructure of the centre may be divided into two primary subsystems that can run together actual timing and HIL simulates. The primary system segment consists of 15 RTDScapable layers with about 26–30 similar processor apiece. A system with 5,000 control units and more than 1,000 electrical nodes can enable accurate time simulations (such as measuring units) at time steps of up to 50 s. It should be mentioned that the period of actual time simulation may be decreased to 1 s for time-sensitive systems, including converters for control electronics. The communication connecting the RTS plus actual EPS apparatus is made possible via fiber-optic networks. The testbed’s physical infrastructure includes a 4.16-kV allocation mechanism, a 7.5-MVA on-site service transformer, a 5-MW variable voltage and variable frequency converter, a 5-MW dynamometer, and a 1.5-MVA experimental bus at 480 Vac. Three OpalRT-enabled racks are part of the second subsystem, which is also backed by a number of processor units and Xilinx FPGA computation units. Using seconds time-steps, the remaining portions of the EPS are simulated, while the high-frequency power electronic converters are simulated using FPGA hardware accelerators under strict timing restrictions (i.e., in the nanosecond range). Multiple industrial protocols that are used for communications between real-world or virtual EPS assets are supported by both subsystems. HIL simulations are also useful for experimenting with communication network components and advanced control strategies. Additionally, a controlled environment with low risk may be used to investigate the effects of unforeseen failures or cyberattacks directed at these components [12].

1.2 Studies on CPES Safety

Along with the aim of improving CPES resilience with reducing cyber safety risks, tremendous attempt has been put into CPES security research during the last 10 years—for instance, presenting a thorough analysis of cybersecurity issues and potential fixes for smart grid implementations. Evaluation of security solutions, categorization of system threats, and upcoming cybersecurity research directions are also taken into account. The authors look at cyberattacks on grid deployments that are IoT-enabled. They discuss how improvements in IoT technology may accelerate the modernization of the power grid but also raise the danger surface of the system due to its linked architecture involving millions of IoT nodes. In [13], researchers look at the security of contemporary power systems in relation to their connectivity to micro grids. The difficulties posed by these designs in terms of cybersecurity and dependability are highlighted. To improve the security of next-generation power systems, key strategies (such as testbed-assisted security studies) are highlighted. Furthermore, it offers a thorough review of the digital risks that power system structure, net protocols, and request layers have to deal with. Additionally, threats aimed at compromising the micro grids’ data availability, integrity, and confidentiality were discussed. In this part, we summarize the key points in the body of research on CPES security. The following categories are used to more precisely classify the literary work: research looking at how to exploit CPES weaknesses, studies examining how cyberattacks affect CPES, studies recommending and evaluating techniques for the finding of cyberattacks as well as studies concentrating on improvement and defensive measures.

1.2.1 Attacks in the CPES System

Cyberattack using security flaws in CPES is required to manage rising authority requirements even with the reduction of communication and distribution losses. CPES is moving toward decentralized linked systems, using MG installations and their functions (such as matrix or independent activities), and incorporating DERs. Additionally, digital ICT tools like enhanced measurement and surveillance devices are being used in globally diverse sites of decentralized CPES in order to improve management, dependability, and protection. PMUs, for instance, offer timesynchronized (through GPS) granularity observations for EPS-related states, such as amplitudes of voltage, current, and power as well as phasing angles. The ability of attackers to use expansive, publicly accessible tools to carry out GPS faking operations on PMUs has been shown. The sequence discrepancies among exact and evaluated vantage points can be dramatically altered beyond the permitted limit by making slight, unnoticeable scheduling disruptions (in the seconds scope) in the extent associated with them (under the mentioned C37.118 IEEE standard constraints). This can trip electrical systems (CBs) and sectionalize the EPS, resulting in power outages. Investigators also provide a planned and load distribution attack that affects energy dispatch algorithms.

Environmental control systems are tricked into higher load reduction by assaulting units or distribution ranks even as fabricating load requirements and line energy flow. Additionally, the researchers look at two different types of DoS attacks and how they affect EPS. In order to hide the assault effect from detection techniques, the initial attack is thought to be a covert fake data strategy. The second attack, which is regarded as a non-stealthy one, targets the power line that is most susceptible to damage, slows down power delivery, and results in power outages in an effort to maximize the harm to the functioning of the power grid. The authors suggest attacks that combine data access with the integrity of information. They show how control centre measurements may be changed to produce FDIAs that are undetected. More specifically, FDIAs may circumvent flawed data detection methods by changing some countermeasures (such as an integration attack) while blocking access to some others for the state estimation process (i.e., accessibility assault).

The operation of CPES is faced with additional difficulties due to ubiquitous electrical machines. Power converter systems are anticipated to dominate future CPES. As a result, flaws in such components may cause the system to operate strangely. The authors examine how covert, non-intrusive assaults on grid-tied inverters might impair their intended performance and have an adverse effect on grid operation. They specifically demonstrate variations in output voltage, active and reactive power as well as adding reduced aberrations to the grid by fooling the hall sensor of the inverter. Similarly, the authors show how local or remote attackers can obtain weakly encoded password hashes, which can then be overturned to allow unauthorized access. This is accomplished by using vulnerability in the standard electric multiline safety and managing a strategy’s authenticator. Additionally, researchers throughout demonstrate how the EPS’s power reserve restrictions may be surpassed by synchronizing the power use of many devices, which can trip lines and shed a lot. A botnet comprising large connected IoT appliances, including air conditioners, dryers, and washers, is organized across the internet, resulting in unforeseen power demand patterns and straining the grid to its breaking point. Such assaults show that neither a significant attack resources nor an in-depth adversarial expertise is necessary.

1.2.2 Evaluation of Attack Impacts on CPES

Prioritizing and securing important CPES elements is thought to require impact assessment and assessment activities. Such evaluations investigate the effects of harmful assaults and may help networks be actively ready for potential negative effects. Effect analyses may highlight important elements of an entire system, help to prioritize and secure them, and support the creation of contingency systems in the event that these exposed, susceptible elements are hacked—for example, the researchers provide an evaluation criteria to gauge the CPES’s resistance to hostile assaults. In order to evaluate the capacity of CPES while maintaining essential (or unshed able) workloads when they have been damaged or the network has experienced unforeseen shocks, many methodologies from behavioral economics, discrete mathematics, and stochastic modeling have been used. In other research, the effect of assaults on reconfigurable power systems (TES) is analyzed. Here the authors look at how the system operates when it is subjected to two different forms of assault that aim to intentionally alter either the bid prices or the bid amounts. IEDs, AMIs, and smart converters are rapidly encroaching into EPS; hence, the researchers show the negative grid effects if any such devices are hacked. In particular, the replicated effects about fraudulent intelligent converter software alterations in MGs are demonstrated. In an integrative T&D system paradigm, assaults against application power switches or surveillance systems that hinder spatial awareness are examined in [14].

The research also looks at vulnerability evaluation methods that examine the effects of RES connectivity with the grid—for example, to identify the most important system routes, the authors use open-source information and scenario analytical techniques. An attacker may use these transition channels to increase the effect of cyberattacks, which would be terrible for the EPS. A probabilistic match theory based CPES safety valuation replica is created in, which proposes an alternative strategy that takes into account the modeling of the infiltration and interruption processes. The researchers provide a statistical methodology to calculate the likelihood of malware activity on grid process automation and assess its effects. A poor standard evaluation process is used to examine the integrity and dependability of CPES. In general, approaches for assessing attack effects on CPES are created to support CPES comparative analyses. Therefore, they must be used for determining various risks and creating CPES defensive measures as a component of a defence and security portfolio.

1.2.3 CPES’s Assault Detection Algorithms

The seriousness of the impacts of assaults on CPES emphasizes the necessity on behalf of precise as well as efficient threat identification techniques that may interact to structure managers’ spatial awareness. As a result, corrective measures may be taken to guarantee safe operation as well as the avoidance of system and equipment failures. Numerous sensors, specifically for FDIAs in CPES, have been put forward. As an example, scientists built a networked server collective approach for PMUs to identify erroneous data readings. Each PMU has a host monitoring system attached to it that may check if it is operating normally or if there is an anomaly by comparing it to established theoretical values. The state of the PMU under inquiry is then compared to that of the equivalent nearby PMUs, and a qualified majority mechanism is then used to determine if the measures gathered are legitimate. For the purpose of detecting cyberattacks in CPES, autonomous attempts to learn abnormality identification techniques have also been put out. Repetitive neural networks are used by the researchers to recognize questionable sensing behavior as an instance of such an abnormality detection scheme (RNNs). File integrity assaults (DIA) may be recognized by other investigators when sensing and processing trends diverge from leftover frisked data. Additionally, methods have been developed to identify unusual network activity at a number of Fieldbus interfaces due to the widespread usage of Fieldbus communications technology in CPES. The goal of each of the examined detection methods is to alert system administrators when erroneous sensors or monitoring activity is found in the CPES. Therefore, harmful situations may be dealt with efficiently, reducing the influence they have on CPES activities.

1.2.4 CPES’s Assault Mitigation and Defense Systems

To improve the overall protection of the CPES and lessen the negative effects of cyber-attack eventualities, defence and prevention methods must be deployed. As an illustration, preventative tactics may shield CPES from FDIAs that can harm generating hardware. In particular, BESS may be used to support the reactors and lessen load restrictions brought on by harmful assaults. Similarly, the research describes a hybrid control-based strategy for defending computers from online threats. Considering that some of these devices may have been penetrated by an attacker, the composite regulator selects the most secured controllers from among a subset of those that are accessible. In order to identify malicious trends and protect the system against unidentified spyware that targets the CPES architecture, uses a tractor-trailer learning approach. Equipment-oriented approaches were also suggested in addition to application-oriented preventive strategies and countermeasures. The authors suggest using dedicated hardware blocks that take advantage of the inherent diversity in BESS lithium batteries to improve network protocol integrity. In a virtual testbed setting, the approach’s viability is proven. Additionally, the author presents an instrumentation-based defensive strategy that secures CPES in real time while using a less-than-ideal design. The protection of CPES is improved through study and advancements in this area, even if the stated prevention and improvement techniques might not be relevant every cyberattack situations. These efforts aid in comprehending adversary tactics and combating them.

Risk assessments and vulnerability assessments investigating intricate CPES infrastructures, identifying possible weaknesses, and thoroughly testing and evaluating security mechanisms all need accurate modeling. The complexity of a CPS often consists of many convolutional nodes that integrate resources with varied degrees of relevance to the state’s functioning and make use of ICT and communicating technologies. Various methodologies are being utilized to evaluate the privacy of CPS designs. The most often used models are DiD and Purdue among them. The Did approach is first used in armed contexts. It guarantees resilience, diversity, plus presence about numerous defences in the event of a weakness being exposed, the discovery of a serious safety issue, a malfunction, or an inadvertent error. From a safety perspective, implementing the DiD inter-layered topology offers two key benefits. First off, because each layer offers a separate operating system, it slows down the attack’s progression inside the system. Furthermore, it frees network administrators from needing to depend on a single point of protection and enables them to handle the assault separately across many levels. Comparable to this, the Purdue model for ICS packet filtering, which is an element of the Purdue Enterprises Architecture, integrates the DiD notion via showing how levels as well as elements are interconnected and dependent on one another, enabling the construction of safe CPS [15]. With a focus on commercial CPS and infrastructure systems, the next portions give necessary details along with associated effort addressing risk modeling and threat evaluation approaches.

1.2.5 Dangerous Imagery

The process by which possible flaws are found before they can constitute systemic concerns is referred to as “vulnerability assessments.” The development of network security and countermeasures must follow this method. It is clear that vulnerability assessment for CPES is important given that their vulnerability might have severe repercussions for grid functioning as well as for the economy and societal well-being. Nevertheless, since CPES consists of several levels and components, it might be difficult to thoroughly investigate every situation that can develop as vulnerability because doing so would take a lot of time, modeling work, personnel, and money. Numerous malware detection methodologies have been put out in an effort to prioritize flaws and aid in the installation of effective safeguards in order to address these problems without jeopardizing the state’s dependability.

By emphasizing the important assets, sometimes known as the system’s “crown jewels,” and evaluating threats according to their potential effect and ease of implementation, these techniques provide users with a comprehensive understanding of the system. The well-known threat modeling tools STRIDE2 and DREAD3 are used to assess the safety of goods and services across their life cycles. Data flow diagrams are used by STRIDE, for example, during the threat modeling phase. The diagrams of data flow (STRIDE per-element method) relate system risks to the relevant susceptible parts of the system. Due to the interdependence of CPES, an adversary may jeopardise system functioning by taking advantage of flaws in various components. Consequently, risks must be tackled both at the system level and inside the element interlinkages (visualized in the information flow diagrams) in order to ensure total system security. To assess and rate the seriousness of threats, use DREAD. Asset identification, software architectural creation, programme breakdown, risk recognition, risk description, and risk severity grading are the six processes that make up a DREAD assessment. Robust cybersecurity evaluations may also be performed using a combination of the DREAD and STRIDE approaches. In the security field, other sections of the review approaches than stride and dread have been suggested and used. OCTAVE4 Allegro, for example, is a different strategy that businesses use when conducting information systems (IT) security assessments and long-term cyber-threat planning. Recent studies have, therefore, shown the suitability of OCTAVE Allegro for CPS information protection, together listing of possible hazards as well as for the construction of remedies to sustain method frequency functioning. The formulation of risk assessment criteria in accordance with operational restrictions, the identification of critical assets, the finding of important data weaknesses and possible risks, and the threat effect analysis are the primary phases used in OCTAVE vulnerability scanning. When doing threat modeling assessments and determining flaws in the pre-attack setting, the technologies STRIDE, DREAD, and OCTAVE are widely known. Analysis of opponent actions following compromises is also essential. The enemy has now successfully evaded the primary level protection along with gained contact to technical sources. In particular, a lot of study on the first usage and exploitation of perimeter defences. After first access is obtained, there remains an information break about the adversary process, however. The framework for ATT&CK for enterprise was created by MITRE to solve