Biblio

The new paradigm of industrial development, called Industry 4.0, faces the problems of Cybersecurity, and as it has already manifested itself in Information Systems, focuses on the use of Artificial Intelligence tools. The authors of this article build on their experience with the use of the above mentioned tools to increase the resilience of Information Systems against Cyber threats, approached to the choice of an effective structure of Cyber-protection of Industrial Systems, primarily analyzing the objective differences between them and Information Systems. A number of analyzes show increased resilience of the decentralized architecture in the management of large-scale industrial processes to the centralized management architecture. These considerations provide sufficient grounds for the team of the project to give preference to the decentralized structure with flock behavior for further research and experiments. The challenges are to determine the indicators which serve to assess and compare the impacts on the controlled elements.

This Innovative Practice Work-in-Progress paper presents a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds and enable effective retention and transfer of the multidisciplinary AI-cybersecurity knowledge. While progress has been made to better understand the trustworthiness and security of artificial intelligence (AI) techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. To address this import issue, in our proposed learning paradigm, we leverage multidisciplinary expertise in cybersecurity, AI, and statistics to systematically investigate two cohesive research and education goals. First, we develop an immersive learning environment that motivates the students to explore AI/machine learning (ML) development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. Second, we design a proactive education paradigm with the use of hackathon activities based on game-based learning, lifelong learning, and social constructivism. The proposed paradigm will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. In this paper, we describe our proposed learning paradigm and present our current progress of this ongoing research work. In the current stage, we focus on the first research and education goal and have been leveraging cost-effective Minecraft platform to develop an immersive learning environment where the learners are able to investigate the insights of the emerging AI/ML concepts by constructing related learning modules via interacting with tangible AI/ML building blocks.

The demand for increasing flexibility use in power systems is stressed by the changing grid utilization. Making use of largely untapped flexibility potential is possible through novel flexibility markets. Different approaches for these markets are being developed and vary considering their handling of transaction schemes and relation of participating entities. This paper delivers the conceptual development of a holistic system architecture for the realization of an interregional flexibility market, which targets a market based congestion management in the transmission and distribution system through trading between system operators and flexibility providers. The framework combines a market mechanism with the required supplements like appropriate control algorithms for emergency situations, cyber-physical system monitoring and cyber-security assessment. The resulting methods are being implemented and verified in a remote-power-hardware-in-the-loop setup coupling a real world low voltage grid with a geographically distant real time simulation using state of the art control system applications with an integration of the aforementioned architecture components.

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are paramount. While the number of deployed IoT devices continues to increase annually, they still present severe cyber-security vulnerabilities, turning them into potential targets and entry points to support further attacks. Naturally, as these nodes are compromised, attackers aim at setting up stealthy communication behaviours, to exfiltrate data or to orchestrate nodes of a botnet in a cloaked fashion. Network covert channels are increasingly being used with such malicious intents. The IEEE 802.15.4 is one of the most pervasive protocols in IoT, and a fundamental part of many communication infrastructures. Despite this fact, the possibility of setting up such covert communication techniques on this medium has received very little attention. We aim at analysing the performance and feasibility of such covert-channel implementations upon the IEEE 802.15.4 protocol. This will enable a better understanding of the involved risk and help supporting the development of further cyber-security mechanisms to mitigate this threat.

This paper presents a case study for designing and implementing a secure communication protocol over a Controller Area Network (CAN). The CAN based protocol uses a hybrid encryption method on a relatively simple hardware / software environment. Moreover, the blockchain technology is proposed as a working solution to provide an extra secure level of the proposed system.

The modernization of legacy power grids relies on the prevalence of information technology (IT). While the benefits are multi-fold and include increased reliability, more accurate monitoring, etc., the reliance on IT increases the attack surface of power grids by making them vulnerable to cyber-attacks. One of the modernization paths is the emergence of multi-terminal dc systems that offer numerous advantages over traditional ac systems. Therefore, cyber-security issues surrounding dc networks need to be investigated. Contributing to this effort, a class of false data injection attacks, called load redistribution (LR) attacks, that targets dc grids is proposed. These attacks aim to compromise the system load data and lead the system operator to dispatch incorrect power flow commands that lead to adverse consequences. Although similar attacks have been recently studied for ac systems, their feasibility in the converter-based dc grids has yet to be demonstrated. Such an attack assessment is necessary because the dc grids have a much smaller control timescale and are more dependent on IT than their traditional ac counterparts. Hence, this work formulates and evaluates dc grid LR attacks by incorporating voltage-sourced converter (VSC) control strategies that appropriately delineate dc system operations. The proposed attack strategy is solved with Gurobi, and the results show that both control and system conditions can affect the success of an LR attack.

Malware created by the Advanced Persistent Threat (APT) groups do not typically carry out the attacks in a single stage. The “Cyber Kill Chain” framework developed by Lockheed Martin describes an APT through a seven stage life cycle [5] . APT groups are generally nation state actors [1] . They perform highly targeted attacks and do not stop until the goal is achieved [7] . Researchers are always working toward developing a system and a process to create an environment safe from APT type attacks [2] . In this paper, the threat considered is ransomware which are developed by APT groups. WannaCry is an example of a highly sophisticated ransomware created by the Lazurus group of North Korea and its level of sophistication is evident from the existence of a contingency plan of attack upon being discovered [3] [6] . The major contribution of this research is the analysis of APT type ransomware using game theory to present optimal strategies for the defender through the development of equilibrium solutions when faced with APT type ransomware attack. The goal of the equilibrium solutions is to help the defender in preparedness before the attack and in minimization of losses during and after the attack.

Over the years, human errors have been identified as one of the most critical factors impacting cybersecurity in an organization that has had a substantial impact. The research uses recent articles published on human resources and information cybersecurity. This research focuses on the vulnerabilities and the best solution to mitigate these threats based on literature review methodology. The study also focuses on identifying the human attitude and behavior towards cybersecurity and how that would impact the organization's financial impact. With the help of the Two-factor Taxonomy of the security behavior model developed in past research, the research aims to identify the best practices and compare the best practices with that of the attitude-behavior found and matched to the model. Finally, the study would compare the difference between best practices and the current practices from the model. This would help provide the organization with specific recommendations that would help change their attitude and behavior towards cybersecurity and ensure the organization is not fearful of the cyber threat of human error threat.

The increasing penetration of cyber systems into smart grids has resulted in these grids being more vulnerable to cyber physical attacks. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. However, a system operator can leverage private information about the underlying network to maintain a strategic advantage over an adversary equipped with superior computational capability and situational awareness. In this work, we examine the following scenario: A malicious entity intrudes the cyber-layer of a power network and trips the transmission lines. The objective of the system operator is to deploy security measures in the cyber-layer to minimize the impact of such attacks. Due to budget constraints, the attacker and the system operator have limits on the maximum number of transmission lines they can attack or defend. We model this adversarial interaction as a resource-constrained attacker-defender game. The computational intractability of solving large security games is well known. However, we exploit the approximately modular behaviour of an impact metric known as the disturbance value to arrive at a linear-time algorithm for computing an optimal defense strategy. We validate the efficacy of the proposed strategy against attackers of various capabilities and provide an algorithm for a real-time implementation.

We present a simple approach for detecting brute force attacks in the CSE-CIC-IDS2018 Big Data dataset. We show our approach is preferable to more complex approaches since it is simpler, and yields stronger classification performance. Our contribution is to show that it is possible to train and test simple Decision Tree models with two independent variables to classify CSE-CIC-IDS2018 data with better results than reported in previous research, where more complex Deep Learning models are employed. Moreover, we show that Decision Tree models trained on data with two independent variables perform similarly to Decision Tree models trained on a larger number independent variables. Our experiments reveal that simple models, with AUC and AUPRC scores greater than 0.99, are capable of detecting brute force attacks in CSE-CIC-IDS2018. To the best of our knowledge, these are the strongest performance metrics published for the machine learning task of detecting these types of attacks. Furthermore, the simplicity of our approach, combined with its strong performance, makes it an appealing technique.

The rapid development of the Industry 4.0 initiative highlights the problems of Cyber-security of Industrial Computer Systems and, following global trends in Cyber Defense, the implementation of Artificial Intelligence instruments. The authors, having certain achievement in the implementation of Artificial Intelligence tools in Cyber Protection of Information Systems and, more precisely, creating and successfully experimenting with a hybrid model of Intrusion Detection and Prevention System (IDPS), decided to study and experiment with the possibility of applying a similar model to Industrial Control Systems. This raises the question: can the experience of applying Artificial Intelligence methods in Information Systems, where this development went beyond the experimental phase and has entered into the real implementation phase, be useful for experimenting with these methods in Industrial Systems.

Software vulnerabilities have become a major problem for the security analysts, since the number of new vulnerabilities is constantly growing. Thus, there was a need for a categorization system, in order to group and handle these vulnerabilities in a more efficient way. Hence, the MITRE corporation introduced the Common Weakness Enumeration that is a list of the most common software and hardware vulnerabilities. However, the manual task of understanding and analyzing new vulnerabilities by security experts, is a very slow and exhausting process. For this reason, a new automated classification methodology is introduced in this paper, based on the vulnerability textual descriptions from National Vulnerability Database. The proposed methodology, combines textual analysis and tree-based machine learning techniques in order to classify vulnerabilities automatically. The results of the experiments showed that the proposed methodology performed pretty well achieving an overall accuracy close to 80%.

Cyber-attacks toward cyber-physical systems are one of the main concerns of smart grid's operators. However, many of these cyber-attacks, are toward unmanned substations where the cyber-attackers needs to be close enough to substation to malfunction protection and control systems in substations, using Electromagnetic signals. Therefore, in this paper, a new threat detection algorithm is proposed to prevent possible cyber-attacks toward unmanned substations. Using surveillance camera's streams and based on You Only Look Once (YOLO) V3, suspicious objects in the image are detected. Then, using Intersection over Union (IOU) and Generalized Intersection Over Union (GIOU), threat distance is estimated. Finally, the estimated threats are categorized into three categories using color codes red, orange and green. The deep network used for detection consists of 106 convolutional layers and three output prediction with different resolutions for different distances. The pre-trained network is transferred from Darknet-53 weights trained on 80 classes.

In this paper, we present a theoretical approach concerning the econometric modelling for the estimation of cyber-security risk, with the use of time-series analysis methods and alternatively with Machine Learning (ML) based, deep learning methodology. Also we present work performed in the framework of SAINT H2020 Project [1], concerning innovative data mining techniques, based on automated web scrapping, for the retrieving of the relevant time-series data. We conclude with a review of emerging challenges in cyber-risk assessment brought by the rapid development of adversarial AI.

The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis.

In dynamic control centers, conventional SCADA systems are enhanced with novel assistance functionalities to increase existing monitoring and control capabilities. To achieve this, different key technologies like phasor measurement units (PMU) and Digital Twins (DT) are incorporated, which give rise to new cyber-security challenges. To address these issues, a four-stage threat analysis approach is presented to identify and assess system vulnerabilities for novel dynamic control center architectures. For this, a simplified risk assessment method is proposed, which allows a detailed analysis of the different system vulnerabilities considering various active and passive cyber-attack types. Qualitative results of the threat analysis are presented and discussed for different use cases at the control center and substation level.

This paper describes the architecture and the fundamental methodology of an anomaly detector, which by continuously monitoring Simple Network Management Protocol data and by processing it as complex-events, is able to timely recognize patterns of faults and relevant cyber-attacks. This solution has been applied in the context of smart grids, and in particular as part of a security and resilience component of the Information and Communication Technologies (ICT) Gateway, a middleware-based architecture that correlates and fuses measurement data from different sources (e.g., Inverters, Smart Meters) to provide control coordination and to enable grid observability applications. The detector has been evaluated through experiments, where we selected some representative anomalies that can occur on the ICT side of the energy distribution infrastructure: non-malicious faults (indicated by patterns in the system resources usage), as well as effects of typical cyber-attacks directed to the smart grid infrastructure. The results show that the detection is promisingly fast and efficient.

The need of more secured and environmental energy is becoming a necessity and priority in an environment suffering from serious problems due to technological development. Since the Smart Grid is a promising alternative that supports green energy and enhances a better management of electricity, the security side has became one of the major and critical associated issues in building the communication network in the microgrid.In this paper we will present the Smart Grid Cyber security challenges and propose a distributed algorithm that face one of the biggest problems threatening the smart grid which is fires.

Phasor measurement unit (PMU) networks are increasingly deployed to offer timely and high-precision measurement of today's highly interconnected electric power systems. To enhance the cyber-resilience of PMU networks against malicious attacks and system errors, we develop an optimization-based network management scheme based on the software-defined networking (SDN) communication infrastructure to recovery PMU network connectivity and restore power system observability. The scheme enables fast network recovery by optimizing the path generation and installation process, and moreover, compressing the SDN rules to be installed on the switches. We develop a prototype system and perform system evaluation in terms of power system observability, recovery speed, and rule compression using the IEEE 30-bus system and IEEE 118-bus system.

This paper uses Endsley's situational awareness model as a starting point for creating a new cyber-security awareness model for risk maturity. This is used to model the relationship between risk management-based situational awareness and levels of maturity in making decisions to deal with potential cyber-attacks. The risk maturity related to cyber situational awareness using the fuzzy failure mode effect analysis (FMEA) method is needed as a basis for effective risk-based decision making and to measure the level of maturity in decision making using the Software Engineering Institute Capability Maturity Model Integration (SEI CMMI) approach. The novelty of this research is that it builds a model of the relationship between the level of maturity and the level of risk in cyber-situational awareness. Based on the data during the COVID-19 pandemic, there was a decrease in the number of incidents, including the following decreases: from 15-29 cases of malware attacks to 8-12 incidents, from 20-35 phishing cases to 12-15 cases and from 5-10 ransomware cases to 5-6 cases.

As organizations increasingly view information as one of their most valuable assets, which supports the creation and distribution of their products and services, information security will be an integral part of the design and operation of organizational business processes. Yet, risks associated with cyber-attacks are on the rise. Organizations that are subjected to attacks can suffer significant reputational damage as well as loss of information and knowledge. As a consequence, effective leadership is cited as a critical factor for ensuring corporate level attention for information security. However, there is a lack of empirical understanding as to the roles strategic leaders play in shaping and supporting the cyber-security strategy. This article seeks to address this gap in the literature by focusing on how senior leaders support the cyber-security strategy. The authors conducted a series of exploratory interviews with leaders in the positions of Chief Information Officer, Chief Security Information Officer, and Chief Technology Officer. The findings revealed that leaders are engaged in both transitional, where the focus is on improving governance and integration and transformational support, which involves fostering a new cultural mindset for cyber-resiliency and the development of an ecosystem approach to security thinking.

Cyber-power resiliency analysis of the distribution system is becoming critical with increase in adverse cyberevents. Distribution network operators need to assess and analyze the resiliency of the system utilizing the analytical tool with a carefully designed visualization and be driven by data and model-based analytics. This work introduces the Cyber-Physical Security Assessment Metric (CP-SAM) visualization tool to assist operators in ensuring the energy supply to critical loads during or after a cyber-attack. CP-SAM also provides decision support to operators utilizing measurement data and distribution power grid model and through well-designed visualization. The paper discusses the concepts of cyber-physical resiliency, software design considerations, open-source software components, and use cases for the tool to demonstrate the implementation and importance of the developed tool.

In this paper, the Authors present MQTT (ISO/IEC 20922), coupled with Public-key Infrastructure (PKI) as being highly suited to the secure and timely delivery of the command and control messages required in a low-latency Automated Demand Response (ADR) system which makes use of domestic-level electrical loads connected to the Internet. Several use cases for ADR are introduced, and relevant security considerations are discussed; further emphasizing the suitability of the proposed infrastructure. The authors then describe their testbed platform for testing ADR functionality, and finally discuss the next steps towards getting these kinds of technologies to the next stage.

Internet Service Providers (ISPs) have an economic and operational interest in detecting malicious network activity relating to their subscribers. However, it is unclear what kind of traffic data an ISP has available for cyber-security research, and under which legal conditions it can be used. This paper gives an overview of the challenges posed by legislation and of the data sources available to a European ISP. DNS and NetFlow logs are identified as relevant data sources and the state of the art in anonymization and fingerprinting techniques is discussed. Based on legislation, data availability and privacy considerations, a practically applicable anonymization policy is presented.

Today’s rapidly changing world, is observing fast development of QR-code and Blockchain technologies. It is worth noting that these technologies have also received a boost for sharing. The user gets the opportunity to receive / send funds, issue invoices for payment and transfer, for example, Bitcoin using QR-code. This paper discusses the security of using the symbiosis of Blockchain and QR-code technologies, and the vulnerabilities that arise in this case. The following vulnerabilities were considered: fake QR generators, stickers for cryptomats, phishing using QR-codes, create Malicious QR-Codes for Hack Phones and Other Scanners. The possibility of creating the following malicious QR codes while using the QRGen tool was considered: SQL Injections, XSS (Cross-Site Scripting), Command Injection, Format String, XXE (XML External Entity), String Fuzzing, SSI (Server-Side Includes) Injection, LFI (Local File Inclusion) / Directory Traversal.