Biblio

In this study, we propose a platform upon which a cyber security exercise environment can be built efficiently for national critical infrastructure protection, i.e. a cyber-physical battlefield (CPB), to simulate actual ICS/SCADA systems in operation. Among various design considerations, this paper mainly discusses scalability, mobility, reality, extensibility, consideration of the domain or vendor specificities, and the visualization of physical facilities and their damage as caused by cyber attacks. The main purpose of the study was to develop a platform that can maximize the coverage that encompasses such design considerations. We discuss the construction of the platform through the final design choices. The features of the platform that we attempt to achieve are closely related to the target cyber exercise format. Design choices were made considering the construction of a realistic ICS/SCADA exercise environment that meets the goals and matches the characteristics of the Cyber Conflict Exercise (CCE), an annual national exercise organized by the National Security Research Institute (NSR) of South Korea. CCE is a real-time attack-defense battlefield drill between 10 red teams who try to penetrate a multi-level organization network and 16 blue teams who try to defend the network. The exercise platform provides scalability and a significant degree of freedom in the design of a very large-scale CCE environment. It also allowed us to fuse techniques such as 3D-printing and augmented reality (AR) to achieve the exercise goals. This CPB platform can also be utilized in various ways for different types of cybersecurity exercise. The successful application of this platform in Locked Shields 2018 (LS18) is strong evidence of this; it showed the great potential of this platform to integrate high-level strategic or operational exercises effectively with low-level technical exercises. This paper also discusses several possible improvements of the platform which could be made for better integration, as well as various exercise environments that can be constructed given the scalability and extensibility of the platform.

Energy Distribution Grids are considered critical infrastructure, hence the Distribution System Operators (DSOs) have developed sophisticated engineering practices to improve their resilience. Over the last years, due to the "Smart Grid" evolution, this infrastructure has become a distributed system where prosumers (the consumers who produce and share surplus energy through the grid) can plug in distributed energy resources (DERs) and manage a bi-directional flow of data and power enabled by an advanced IT and control infrastructure. This introduces new challenges, as the prosumers possess neither the skills nor the knowledge to assess the risk or secure the environment from cyber-threats. We propose a simple and usable approach based on the Reference Model of Information Assurance & Security (RMIAS), to support the prosumers in the selection of cybesecurity measures. The purpose is to reduce the risk of being directly targeted and to establish collective responsibility among prosumers as grid gatekeepers. The framework moves from a simple risk analysis based on security goals to providing guidelines for the users for adoption of adequate security countermeasures. One of the greatest advantages of the approach is that it does not constrain the user to a specific threat model.

The constant changing of technologies have brought to critical infrastructure organisations numerous information security threats such as insider threat. Critical infrastructure organisations have difficulties to early detect and capture the possible vital signs of insider threats due sometimes to lack of effective methodologies or frameworks. It is from this viewpoint that, this paper proposes a symptomatic insider threat risk assessments framework known as Insider Threat Framework for Namibia Critical Infrastructure Organization (ITFNACIO), aimed to predict the probable signs of insider threat based on Symptomatic Analysis (SA), and develop a prototype as a proof of concept. A case study was successfully used to validate and implement the proposed framework; hence, qualitative methodology was employed throughout the whole research process where two (2) insider threats were captured. The proposed insider threat framework can be further developed in multiple cases and a more automated system able to trigger an early warning system of possible insider threat events.

The increasing integration of information and communication technologies has undoubtedly boosted the efficiency of Critical Infrastructures (CI). However, the first wave of IoT devices, together with the management of enormous amount of data generated by modern CIs, has created serious architectural issues. While the emerging Fog and Multi-Access Edge Computing (FMEC) paradigms can provide a viable solution, they also bring inherent security issues, that can cause dire consequences in the context of CIs. In this paper, we analyze the applications of FMEC solutions in the context of CIs, with a specific focus on related security issues and threats for the specific while broad scenarios: a smart airport, a smart port, and a smart offshore oil and gas extraction field. Leveraging these scenarios, a set of general security requirements for FMEC is derived, together with crucial research challenges whose further investigation is cornerstone for a successful adoption of FMEC in CIs.

Protecting critical infrastructure from cyberattacks by other nations is a matter of considerable concern. Can deterrence play a role in such protection? Can lessons from nuclear deterrence-the most elaborated and successful version of deterrence-be adapted to the cyber case? Currently, little overlap exists between the two, although that might change in the aftermath of an extensive, destructive cyberattack. The most effective way to protect the cyber-dependent infrastructure is a comprehensive defense (deterrence by denial), which was impractical in the nuclear regime. However, this approach presents challenges. Existing legal norms, particularly those related to controlling collateral damage, might provide some deterrence. Another option might be a new international agreement, but that would involve several difficult issues.

SCADA systems are being constantly migrated to modern information and communication technologies (ICT) -based systems named cyber-physical systems. Unfortunately, this allows attackers to execute exploitation techniques into these architectures. In addition, ransomware insertion is nowadays the most popular attacking vector because it denies the availability of critical files and systems until attackers receive the demanded ransom. In this paper, it is analysed the risk impact of ransomware insertion into SCADA systems and it is suggested countermeasures addressed to the protection of SCADA systems and its components to reduce the impact of ransomware insertion.

This paper presents a control strategy for Cyber-Physical System defense developed in the framework of the European Project ATENA, that concerns Critical Infrastructure (CI) protection. The aim of the controller is to find the optimal security configuration, in terms of countermeasures to implement, in order to address the system vulnerabilities. The attack/defense problem is modeled as a multi-agent general sum game, where the aim of the defender is to prevent the most damage possible by finding an optimal trade-off between prevention actions and their costs. The problem is solved utilizing Reinforcement Learning and simulation results provide a proof of the proposed concept, showing how the defender of the protected CI is able to minimize the damage caused by his her opponents by finding the Nash equilibrium of the game in the zero-sum variant, and, in a more general scenario, by driving the attacker in the position where the damage she/he can cause to the infrastructure is lower than the cost it has to sustain to enforce her/his attack strategy.

Critical infrastructures have suffered from different kind of cyber attacks over the years. Many of these attacks are performed using malwares by exploiting the vulnerabilities of these resources. Smart power grid is one of the major victim which suffered from these attacks and its SCADA system are frequently targeted. In this paper we describe our proposed framework to analyze smart power grid, while its SCADA system is under attack by malware. Malware propagation and its effects on SCADA system is the focal point of our analysis. OMNeT++ simulator and openDSS is used for developing and analyzing the simulated smart power grid environment.

The cyber threat landscape is a constantly morphing surface; the need for cyber defenders to develop and create proactive threat intelligence is on the rise, especially on critical infrastructure environments. It is commonly voiced that Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICS) are vulnerable to the same classes of threats as other networked computer systems. However, cyber defense in operational ICS is difficult, often introducing unacceptable risks of disruption to critical physical processes. This is exacerbated by the notion that hardware used in ICS is often expensive, making full-scale mock-up systems for testing and/or cyber defense impractical. New paradigms in cyber security have focused heavily on using deception to not only protect assets, but also gather insight into adversary motives and tools. Much of the work that we see in today's literature is focused on creating deception environments for traditional IT enterprise networks; however, leveraging our prior work in the domain, we explore the opportunities, challenges and feasibility of doing deception in ICS networks.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.

As modern societies become more dependent on IT services, the potential impact both of adversarial cyberattacks and non-adversarial service management mistakes grows. This calls for better cyber situational awareness-decision-makers need to know what is going on. The main focus of this paper is to examine the information elements that need to be collected and included in a common operational picture in order for stakeholders to acquire cyber situational awareness. This problem is addressed through a survey conducted among the participants of a national information assurance exercise conducted in Sweden. Most participants were government officials and employees of commercial companies that operate critical infrastructure. The results give insight into information elements that are perceived as useful, that can be contributed to and required from other organizations, which roles and stakeholders would benefit from certain information, and how the organizations work with creating cyber common operational pictures today. Among findings, it is noteworthy that adversarial behavior is not perceived as interesting, and that the respondents in general focus solely on their own organization.

The following topics are dealt with: security of data; risk management; decision making; computer crime; invasive software; critical infrastructures; data privacy; insurance; Internet of Things; learning (artificial intelligence).

Cyber-physical systems (CPS) and their Internet of Things (IoT) components are repeatedly subject to various attacks targeting weaknesses in their firmware. For that reason emerges an imminent demand for secure update mechanisms that not only include specific systems but cover all parts of the critical infrastructure. In this paper we introduce a theoretical concept for a secure CPS device update and verification mechanism and provide information on handling hardware-based security incorporating trusted platform modules (TPM) on those CPS devices. We will describe secure communication channels by state of the art technology and also integrity measurement mechanisms to ensure the system is in a known state. In addition, a multi-level fail-over concept is presented, ensuring continuous patching to minimize the necessity of restarting those systems.

Most of the countries evaluate their energy networks in terms of national security and define as critical infrastructure. Monitoring and controlling of these systems are generally provided by Industrial Control Systems (ICSs) and/or Supervisory Control and Data Acquisition (SCADA) systems. Therefore, this study focuses on the cyber-attack vectors on SCADA systems to research the threats and risks targeting them. For this purpose, TCP/IP based protocols used in SCADA systems have been determined and analyzed at first. Then, the most common cyber-attacks are handled systematically considering hardware-side threats, software-side ones and the threats for communication infrastructures. Finally, some suggestions are given.

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.

Traditional security measures for large-scale critical infrastructure systems have focused on keeping adversaries out of the system. As the Internet of Things (IoT) extends into millions of homes, with tens or hundreds of devices each, the threat landscape is complicated. IoT devices have unknown access capabilities with unknown reach into other systems. This paper presents ongoing work on how techniques in sensor verification and cyber-physical modeling and analysis on bulk power systems can be applied to identify malevolent IoT devices and secure smart and connected communities against the most impactful threats.

Cyber-attacks and intrusions in cyber-physical control systems are, currently, difficult to reliably prevent. Knowing a system's vulnerabilities and implementing static mitigations is not enough, since threats are advancing faster than the pace at which static cyber solutions can counteract. Accordingly, the practice of cybersecurity needs to ensure that intrusion and compromise do not result in system or environment damage or loss. In a previous paper [2], we described the Cyberspace Security Econometrics System (CSES), which is a stakeholder-aware and economics-based risk assessment method for cybersecurity. CSES allows an analyst to assess a system in terms of estimated loss resulting from security breakdowns. In this paper, we describe two new related contributions: 1) We map the Cyberspace Security Econometrics System (CSES) method to the evaluation and mitigation steps described by the NIST Guide to Industrial Control Systems (ICS) Security, Special Publication 800-82r2. Hence, presenting an economics-based and stakeholder-aware risk evaluation method for the implementation of the NIST-SP-800-82 guide; and 2) We describe the application of this tailored method through the use of a fictitious example of a critical infrastructure system of an electric and gas utility.

Cryptographic APIs are often vulnerable to attacks that compromise sensitive cryptographic keys. In the literature we find many proposals for preventing or mitigating such attacks but they typically require to modify the API or to configure it in a way that might break existing applications. This makes it hard to adopt such proposals, especially because security APIs are often used in highly sensitive settings, such as financial and critical infrastructures, where systems are rarely modified and legacy applications are very common. In this paper we take a different approach. We propose an effective method to monitor existing cryptographic systems in order to detect, and possibly prevent, the leakage of sensitive cryptographic keys. The method collects logs for various devices and cryptographic services and is able to detect, offline, any leakage of sensitive keys, under the assumption that a key fingerprint is provided for each sensitive key. We define key security formally and we prove that the method is sound, complete and efficient. We also show that without key fingerprinting completeness is lost, i.e., some attacks cannot be detected. We discuss possible practical implementations and we develop a proof-of-concept log analysis tool for PKCS\#11 that is able to detect, on a significant fragment of the API, all key-management attacks from the literature.

Enhancing the security and resilience of interdependent infrastructures is crucial. In this paper, we establish a theoretical framework based on Markov decision processes (MDPs) to design optimal resiliency mechanisms for interdependent infrastructures. We use MDPs to capture the dynamics of the failure of constituent components of an infrastructure and their cyber-physical dependencies. Factored MDPs and approximate linear programming are adopted for an exponentially growing dimension of both state and action spaces. Under our approximation scheme, the optimally distributed policy is equivalent to the centralized one. Finally, case studies in a large-scale interdependent system demonstrate the effectiveness of the control strategy to enhance the network resilience to cascading failures.

Supervisory control and data acquisition (SCADA) systems are the key driver for critical infrastructures and industrial facilities. Cyber-attacks to SCADA networks may cause equipment damage or even fatalities. Identifying risks in SCADA networks is critical to ensuring the normal operation of these industrial systems. In this paper we propose a Bayesian network-based cyber-security risk assessment model to dynamically and quantitatively assess the security risk level in SCADA networks. The major distinction of our work is that the proposed risk assessment method can learn model parameters from historical data and then improve assessment accuracy by incrementally learning from online observations. Furthermore, our method is able to assess the risk caused by unknown attacks. The simulation results demonstrate that the proposed approach is effective for SCADA security risk assessment.

Cyber Physical Systems (CPS) operating in modern critical infrastructures (CIs) are increasingly being targeted by highly sophisticated cyber attacks. Threat actors have quickly learned of the value and potential impact of targeting CPS, and numerous tailored multi-stage cyber-physical attack campaigns, such as Advanced Persistent Threats (APTs), have been perpetrated in the last years. They aim at stealthily compromising systems' operations and cause severe impact on daily business operations such as shutdowns, equipment damage, reputation damage, financial loss, intellectual property theft, and health and safety risks. Protecting CIs against such threats has become as crucial as complicated. Novel distributed detection and reaction methodologies are necessary to effectively uncover these attacks, and timely mitigate their effects. Correlating large amounts of data, collected from a multitude of relevant sources, is fundamental for Security Operation Centers (SOCs) to establish cyber situational awareness, and allow to promptly adopt suitable countermeasures in case of attacks. In our previous work we introduced three methods for security information correlation. In this paper we define metrics and benchmarks to evaluate these correlation methods, we assess their accuracy, and we compare their performance. We finally demonstrate how the presented techniques, implemented within our cyber threat intelligence analysis engine called CAESAIR, can be applied to support incident handling tasks performed by SOCs.

The growing computerization of critical infrastructure as well as the pervasiveness of computing in everyday life has led to increased interest in secure application development. We observe a flurry of new security technologies like ARM TrustZone and Intel SGX, but a lack of a corresponding architectural vision. We are convinced that point solutions are not sufficient to address the overall challenge of secure system design. In this paper, we outline our take on a trusted component ecosystem of small individual building blocks with strong isolation. In our view, applications should no longer be designed as massive stacks of vertically layered frameworks, but instead as horizontal aggregates of mutually isolated components that collaborate across machine boundaries to provide a service. Lateral thinking is needed to make secure systems going forward.

Conventional cyber defenses require continual maintenance: virus, firmware, and software updates; costly functional impact tests; and dedicated staff within a security operations center. The conventional defenses require access to external sources for the latest updates. The whitelisted system, however, is ideally a system that can sustain itself freed from external inputs. Cyber-Physical Systems (CPS), have the following unique traits: digital commands are physically observable and verifiable; possible combinations of commands are limited and finite. These CPS traits, combined with a trust anchor to secure an unclonable digital identity (i.e., digitally unclonable function [DUF] - Patent Application \#15/183,454; CodeLock), offers an excellent opportunity to explore defenses built on whitelisting approach called “Trustworthy Design Architecture (TDA).” There exist significant research challenges in defining what are the physically verifiable whitelists as well as the criteria for cyber-physical traits that can be used as the unclonable identity. One goal of the project is to identify a set of physical and/or digital characteristics that can uniquely identify an endpoint. The measurements must have the properties of being reliable, reproducible, and trustworthy. Given that adversaries naturally evolve with any defense, the adversary will have the goal of disrupting or spoofing this process. To protect against such disruptions, we provide a unique system engineering technique, when applied to CPSs (e.g., nuclear processing facilities, critical infrastructures), that will sustain a secure operational state without ever needing external information or active inputs from cybersecurity subject-matter experts (i.e., virus updates, IDS scans, patch management, vulnerability updates). We do this by eliminating system dependencies on external sources for protection. Instead, all internal co- munication is actively sealed and protected with integrity, authenticity and assurance checks that only cyber identities bound to the physical component can deliver. As CPSs continue to advance (i.e., IoTs, drones, ICSs), resilient-maintenance free solutions are needed to neutralize/reduce cyber risks. TDA is a conceptual system engineering framework specifically designed to address cyber-physical systems that can potentially be maintained and operated without the persistent need or demand for vulnerability or security patch updates.

Utility networks are part of every nation's critical infrastructure, and their protection is now seen as a high priority objective. In this paper, we propose a threat awareness architecture for critical infrastructures, which we believe will raise security awareness and increase resilience in utility networks. We first describe an investigation of trends and threats that may impose security risks in utility networks. This was performed on the basis of a viewpoint approach that is capable of identifying technical and non-technical issues (e.g., behaviour of humans). The result of our analysis indicated that utility networks are affected strongly by technological trends, but that humans comprise an important threat to them. This provided evidence and confirmed that the protection of utility networks is a multi-variable problem, and thus, requires the examination of information stemming from various viewpoints of a network. In order to accomplish our objective, we propose a systematic threat awareness architecture in the context of a resilience strategy, which ultimately aims at providing and maintaining an acceptable level of security and safety in critical infrastructures. As a proof of concept, we demonstrate partially via a case study the application of the proposed threat awareness architecture, where we examine the potential impact of attacks in the context of social engineering in a European utility company.

Supervisory Control and Data Acquisition(SCADA) communications are often subjected to various sophisticated cyber-attacks mostly because of their static system characteristics, enabling an attacker for easier profiling of the target system(s) and thereby impacting the Critical Infrastructures(CI). In this Paper, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, leveraging the existing communication network with an end-to-end IP-Hopping technique among trusted peers. The main contribution involves the design and implementation of MTD Architecture on Iowa State's PowerCyber testbed for targeted cyber-attacks, without compromising the availability of a SCADA system and studying the delay and throughput characteristics for different hopping rates in a realistic environment. Finally, we study two cases and provide mitigations for potential weaknesses of the proposed mechanism. Also, we propose to incorporate port mutation to further increase attack complexity as part of future work.