Biblio

The need to achieve a suitable level of security in Cyber-Physical Systems (CPS) presents a major challenge for engineers. The unpredictable communication of highly constrained, but safety-relevant systems in a heterogeneous environment, significantly impacts the number and severity of vulnerabilities. Consequently, if security-related weaknesses can successfully be exploited by attackers, the functionality of critical infrastructure could be denied or malfunction. This might consequently threaten life or leak sensitive information. A toolkit to quantitatively express security is essential for security engineers in order to define security-enhancing measurements. For this purpose, security scoring frameworks, like the established Common Vulnerability Scoring System can be used. However, existing security scoring frameworks may not be able to handle the proposed challenges and characteristics of CPS. Therefore, in this work, we aim to elaborate a security scoring system that is tailored to the needs of CPS. In detail, we analyze security on a System-of-Systems level, while considering multiple attacks, as well as potential side effects to other security-related objects. The positive effects of integrated mitigation concepts should also be abbreviated by our proposed security score. Additionally, we generate the security score for interacting AUTOSAR platforms in a highly-connected Vehicle-to-everything (V2x) environment. We refer to this highly relevant use case scenario to underline the benefits of our proposed scoring framework and to prove its effectiveness in CPS.

Performance measures commonly used in systems security engineering tend to be static, linear, and have limited utility in addressing challenges to security performance from increasingly complex risk environments, adversary innovation, and disruptive technologies. Leveraging key concepts from resilience science offers an opportunity to advance next-generation systems security engineering to better describe the complexities, dynamism, and nonlinearity observed in security performance—particularly in response to these challenges. This article introduces a multilayer network model and modified Continuous Time Markov Chain model that explicitly captures interdependencies in systems security engineering. The results and insights from a multilayer network model of security for a hypothetical nuclear power plant introduce how network-based metrics can incorporate resilience concepts into performance metrics for next generation systems security engineering.

Preventing software vulnerabilities while writing code is one of the most effective ways for avoiding cyber attacks on any developed system. Although developers follow some standard guiding principles for ensuring secure code, the code can still have security bottlenecks and be compromised by an attacker. Therefore, assessing software security while developing code can help developers in writing vulnerability free code. Researchers have already focused on metrics-based and text mining based software vulnerability prediction models. The metrics based models showed higher precision in predicting vulnerabilities although the recall rate is low. In addition, current research did not investigate the impact of individual software metric on the occurrences of vulnerabilities. The main objective of this paper is to track the changes in every software metric after the developer fixes a particular vulnerability. The results of our research will potentially motivate further research on building more accurate vulnerability prediction models based on the appropriate software metrics. In particular, we have compared a total of 250 files from Apache Tomcat and Apache CXF. These files were extracted from the Apache database and were chosen because Apache released these files as vulnerable in their publicly available security advisories. Using a static analysis tool, metrics of the targeted vulnerable files and relevant fixed files (files where vulnerable code is removed by the developers) were extracted and compared. We show that eight of the 40 metrics have an average increase of 2% from vulnerable to fixed files. These metrics include CountDeclClass, CountDeclClassMethod, CountDeclClassVariable, CountDeclInstanceVariable, CountDeclMethodDefault, CountLineCode, MaxCyclomaticStrict, MaxNesting. This study will help developers to assess software security through utilizing software metrics in secure coding practices.

With the increasing complexity, modern system-onchip (SoC) designs are becoming more susceptible to security attacks and require comprehensive security assurance. However, establishing a comprehensive assurance for security often involves knowledge of relevant security assets. Since modern SoCs contain myriad confidential assets, the identification of security assets is not straightforward. The number and types of assets change due to numerous embedded hardware blocks within the SoC and their complex interactions. Some security assets are easily identifiable because of their distinct characteristics and unique definitions, while others remain in the blind-spot during design and verification and can be utilized as potential attack surfaces to violate confidentiality, integrity, and availability of the SoC. Therefore, it is essential to automatically identify security assets in an SoC at pre-silicon design stages to protect them and prevent potential attacks. In this paper, we propose an automated CAD framework called SAF to identify an SoC's security assets at the register transfer level (RTL) through comprehensive vulnerability analysis under different threat models. Moreover, we develop and incorporate metrics with SAF to quantitatively assess multiple vulnerabilities for the identified security assets. We demonstrate the effectiveness of SAF on MSP430 micro-controller and CEP SoC benchmarks. Our experimental results show that SAF can successfully and automatically identify an SoC's most vulnerable underlying security assets for protection.

Probabilistic security metrics estimate the vulnerability of a network in terms of the likelihood of an attacker reaching the goal states (of a network) by exploiting the attack graph paths. The probability computation depends upon several assumptions regarding the possible attack scenarios. In this paper, we extend the existing security metric to model networks with intrusion detection systems and their associated uncertainties and time latencies. We consider learning capabilities of attackers as well as detection systems. Estimation of risk is obtained by using the attack paths that are undetectable owing to the latency of the detection system. Thus, we define the overall vulnerability (of a network) as a function of the time window available to an attacker for repeated exploring (via learning) and exploitation of a network, before the attack is mitigated by the detection system. Finally, we consider the realistic scenario where an attacker explores and abandons various partial paths in the attack graph before the actual exploitation. A dynamic programming formulation of the vulnerability computation methodology is proposed for this scenario. The nature of these metrics are explained using a case study showing the vulnerability spectrum from the case of zero detection latency to a no detection scenario.

Embedded Systems (ES) development has been historically focused on functionality rather than security, and today it still applies in many sectors and applications. However, there is an increasing number of security threats over ES, and a successful attack could have economical, physical or even human consequences, since many of them are used to control critical applications. A standardized and general accepted security testing framework is needed to provide guidance, common reporting forms and the possibility to compare the results along the time. This can be achieved by introducing security metrics into the evaluation or assessment process. If carefully designed and chosen, metrics could provide a quantitative, repeatable and reproducible value that would reflect the level of security protection of the ES. This paper analyzes the features that a good security metric should exhibit, introduces a taxonomy for classifying them, and finally, it carries out a literature survey on security metrics for the security evaluation of ES. In this review, more than 500 metrics were collected and analyzed. Then, they were reduced to 169 metrics that have the potential to be applied to ES security evaluation. As expected, the 77.5% of them is related exclusively to software, and only the 0.6% of them addresses exclusively hardware security. This work aims to lay the foundations for constructing a security evaluation methodology that uses metrics so as to quantify the security level of an ES.

Anomaly detection (AD) is very important across several real-world problems in the heavy industries and Internet-of-Things (IoT) domains. Traditional methods so far have categorized anomaly detection into (a) unsupervised, (b) semi-supervised and (c) supervised techniques. A relatively unexplored direction is the development of context aware anomaly detection systems which can build on top of any of these three techniques by using side information. Context can be captured from a different modality such as semantic graphs encoding grouping of sensors governed by the physics of the asset. Process flow diagrams of an operational plant depicting causal relationships between sensors can also provide useful context for ML algorithms. Capturing such semantics by itself can be pretty challenging, however, our paper mainly focuses on, (a) designing and implementing effective anomaly detection pipelines using sparse Gaussian Graphical Models with various statistical distance metrics, and (b) differentiating these pipelines by embedding contextual semantics inferred from graphs so as to obtain better KPIs in practice. The motivation for the latter of these two has been explained above, and the former in particular is well motivated by the relatively mediocre performance of highly parametric deep learning methods for small tabular datasets (compared to images) such as IoT sensor data. In contrast to such traditional automated deep learning (AutoAI) techniques, our anomaly detection system is based on developing semantics-driven industry specific ML pipelines which perform scalable computation evaluating several models to identify the best model. We benchmark our AD method against state-of-the-art AD techniques on publicly available UCI datasets. We also conduct a case study on IoT sensor and semantic data procured from a large thermal energy asset to evaluate the importance of semantics in enhancing our pipelines. In addition, we also provide explainable insights for our model which provide a complete perspective to a reliability engineer.

We consider a two-player zero-sum network routing game in which a router wants to maximize the amount of legitimate traffic that flows from a given source node to a destination node and an attacker wants to block as much legitimate traffic as possible by flooding the network with malicious traffic. We address scenarios with asymmetric information, in which the router must reveal its policy before the attacker decides how to distribute the malicious traffic among the network links, which is naturally modeled by the notion of Stackelberg equilibria. The paper focuses on parallel networks, and includes three main contributions: we show that computing the optimal attack policy against a given routing policy is an NP-hard problem; we establish conditions under which the Stackelberg equilibria lead to no regret; and we provide a metric that can be used to quantify how uncertainty about the attacker's capabilities limits the router's performance.

As networks continue to grow in complexity using wired and wireless technologies, efficient testing solutions should accommodate such changes and growth. Network simulators provide a network-independent environment to provide different types of network testing. This paper is motivated by the observation that, in many cases in the literature, the success of developed network protocols is very sensitive to the initial conditions and assumptions of the testing scenarios. Network services are deployed in complex environments; results of testing and simulation can vary from one environment to another and sometimes in the same environment at different times. Our goal is to propose mutation-based integration testing that can be deployed with network protocols and serve as Built-in Tests (BiT).This paper proposes an integrated mutation testing framework to achieve systematic test cases' generation for different scenario types. Scenario description and variables' setting should be consistent with the protocol specification and the simulation environment. We focused on creating test cases for critical scenarios rather than preliminary or simplified scenarios. This will help users to report confident simulation results and provide credible protocol analysis. The criticality is defined as a combination of network performance metrics and critical functions' coverage. The proposed solution is experimentally proved to obtain accurate evaluation results with less testing effort by generating high-quality testing scenarios. Generated test scenarios will serve as BiTs for the network simulator. The quality of the test scenarios is evaluated from three perspectives: (i) code coverage, (ii) mutation score and (iii) testing effort. In this work, we implemented the testing framework in NS2, but it can be extended to any other simulation environment.

The Common Weakness Enumeration (CWE) is a prominent list of software weakness types. This list is used by vulnerability databases to describe the underlying security flaws within analyzed vulnerabilities. This linkage opens the possibility of using the analysis of software vulnerabilities to identify the most significant weaknesses that enable those vulnerabilities. We accomplish this through creating mashup views combining CWE weakness taxonomies with vulnerability analysis data. The resulting graphs have CWEs as nodes, edges derived from multiple CWE taxonomies, and nodes adorned with vulnerability analysis information (propagated from children to parents). Using these graphs, we develop a suite of metrics to identify the most significant weakness types (using the perspectives of frequency, impact, exploitability, and overall severity).

The numerous attacks on ICS systems have made severe threats to critical infrastructure. Extensive studies have focussed on the risk assessment of discovering vulnerabilities. However, to identify Zero-day vulnerabilities is challenging because they are unknown to defenders. Here we sought to measure ICS system zero-day risk by building an enhanced attack graph for expected attack path exploiting zero-day vulnerability. In this study, we define the security metrics of Zero-day vulnerability for an ICS. Then we created a Zero-day attack graph to guide how to harden the system by measuring attack paths that exploiting zero-day vulnerabilities. Our studies identify the vulnerability assessment method on ICS systems considering Zero-day Vulnerability by zero-day attack graph. Together, our work is essential to ICS systems security. By assessing unknown vulnerability risk to close the imbalance between attackers and defenders.

The coupling of safety-relevant embedded- and cyber-space components to build Cyber-Physical Systems (CPS) extends the functionality and quality in many business domains, while also creating new ones. Prime examples like Internet of Things and Industry 4.0 enable new technologies and extend the service capabilities of physical entities by building a universe of connected devices. In addition to higher complexity, the coupling of these heterogeneous systems results in many new challenges, which should be addressed by engineers and administrators. Here, security represents a major challenge, which may be well addressed in cyber-space engineering, but less in embedded system or CPS design. Although model-based engineering provides significant benefits for system architects, like reducing complexity and automated analysis, as well as being considered as standard methodology in embedded systems design, the aspect of security may not have had a major role in traditional engineering concepts. Especially the characteristics of CPS, as well as the coupling of safety-relevant (physical) components with high-scalable entities of the cyber-space domain have an enormous impact on the overall level of security, based on the introduced side effects and uncertainties. Therefore, we aim to define a model-based security-engineering framework, which is tailored to the needs of CPS engineers. Hereby, we focus on the actual modeling process, the evaluation of security, as well as quantitatively expressing security of a deployed CPS. Overall and in contrast to other approaches, we shift the engineering concepts on a semantic level, which allows to address the proposed challenges in CPS in the most efficient way.

The importance of Cyber-Physical Systems (CPS) gains more and more weight in our daily business and private life. Although CPS build the backbone for major trends, like Industry 4.0 and connected vehicles, they also propose many new challenges. One major challenge can be found in achieving a high level of security within such highly connected environments, in which an unpredictable number of heterogeneous systems with often-distinctive characteristics interact with each other. In order to develop high-level security solutions, system designers must eventually know the current level of security of their specification. To this end, security metrics and scoring frameworks are essential, as they quantitatively express security of a given design or system. However, existing solutions may not be able to handle the proposed challenges of CPS, as they mainly focus on one particular system and one specific attack. Therefore, we aim to elaborate a security scoring mechanism, which can efficiently be used in CPS, while considering all essential information. We break down each system within the CPS into its core functional blocks and analyze a variety of attacks in terms of exploitability, scalability of attacks, as well as potential harm to targeted assets. With this approach, we get an overall assessment of security for the whole CPS, as it integrates the security-state of all interacting systems. This allows handling the presented complexity in CPS in a more efficient way, than existing solutions.

We focus on resilience towards covert attacks on Cyber-Physical Systems (CPS). We define the new k-steerability and l-monitorability control-theoretic concepts. k-steerability reflects the ability to act on every individual plant state variable with at least k different groups of functionally diverse input signals. l-monitorability indicates the ability to monitor every individual plant state variable with £ different groups of functionally diverse output signals. A CPS with k-steerability and l-monitorability is said to be (k, l)-resilient. k and l, when both greater than one, provide the capability to mitigate the impact of covert attacks when some signals, but not all, are compromised. We analyze the influence of k and l on the resilience of a system and the ability to recover its state when attacks are perpetrated. We argue that the values of k and l can be augmented by combining redundancy and diversity in hardware and software techniques that apply the moving target paradigm.

Development of quality object-oriented software contains security as an integral aspect of that process. During that process, a ceaseless burden on the developers was posed in order to maximize the development and at the same time to reduce the expense and time invested in security. In this paper, the authors analyzed metrics for object-oriented software in order to evaluate and identify the relation between metric value and security of the software. Identification of these relations was achieved by study of software vulnerabilities with code level metrics. By using OWASP classification of vulnerabilities and experimental results, we proved that there was relation between metric values and possible security issues in software. For experimental code analysis, we have developed special software called SOFTMET.

The electrical power system is the backbone of our nations critical infrastructure. It has been designed to withstand single component failures based on a set of reliability metrics which have proven acceptable during normal operating conditions. However, in recent years there has been an increasing frequency of extreme weather events. Many have resulted in widespread long-term power outages, proving reliability metrics do not provide adequate energy security. As a result, researchers have focused their efforts resilience metrics to ensure efficient operation of power systems during extreme events. A resilient system has the ability to resist, adapt, and recover from disruptions. Therefore, resilience has demonstrated itself as a promising concept for currently faced challenges in power distribution systems. In this work, we propose an operational resilience metric for modern power distribution systems. The metric is based on the aggregation of system assets adaptive capacity in real and reactive power. This metric gives information to the magnitude and duration of a disturbance the system can withstand. We demonstrate resilience metric in a case study under normal operation and during a power contingency on a microgrid. In the future, this information can be used by operators to make more informed decisions based on system resilience in an effort to prevent power outages.

Physical Unclonable Function is an innovative hardware security primitives that exploit the physical characteristics of a physical object to generate a unique identifier, which play the role of the object's fingerprint. Silicon PUF, a popular type of PUFs, exploits the variation in the manufacturing process of integrated circuits (ICs). It needs an input called challenge to generate the response as an output. In addition, of classical attacks, PUFs are vulnerable to physical and modeling attacks. The performance of the PUFs is measured by several metrics like reliability, uniqueness and uniformity. So as an evidence, the main goal is to provide a complete tool that checks the strength and quantifies the performance of a given physical unconscionable function. This paper provides a tool and develops a set of metrics that can achieve safely the proposed goal.

Software Quality Testing has always been a crucial part of the software development process and lately, there has been a rise in the usage of testing applications. While a well-planned and performed test, regardless of its nature - automated or manual, is a key factor when deciding on the results of the test, it is often not enough to give a more deep and thorough view of the whole process. That can be achieved with properly selected software metrics that can be used for proper risk assessment and evaluation of the development.This paper considers the most commonly used metrics when measuring a performed test and examines metrics that can be applied in the development process.

Over the years, a number of vulnerability scoring frameworks have been proposed to characterize the severity of known vulnerabilities in software-dependent systems. These frameworks provide security metrics to support decision-making in system development and security evaluation and assurance activities. When used in this context, it is imperative that these security metrics be sound, meaning that they can be consistently measured in a reproducible, objective, and unbiased fashion while providing contextually relevant, actionable information for decision makers. In this paper, we evaluate the soundness of the security metrics obtained via several vulnerability scoring frameworks. The evaluation is based on the Method for DesigningSound Security Metrics (MDSSM). We also present several recommendations to improve vulnerability scoring frameworks to yield more sound security metrics to support the development of secure software-dependent systems.

The usage of connected devices and their role within our daily- and business life gains more and more impact. In addition, various derivations of Cyber-Physical Systems (CPS) reach new business fields, like smart healthcare or Industry 4.0. Although these systems do bring many advantages for users by extending the overall functionality of existing systems, they come with several challenges, especially for system engineers and architects. One key challenge consists in achieving a sufficiently high level of security within the CPS environment, as sensitive data or safety-critical functions are often integral parts of CPS. Being system of systems (SoS), CPS complexity, unpredictability and heterogeneity complicate analyzing the overall level of security, as well as providing a way to detect ongoing attacks. Usually, security metrics and frameworks provide an effective tool to measure the level of security of a given component or system. Although several comprehensive surveys exist, an assessment of the effectiveness of the existing solutions for CPS environments is insufficiently investigated in literature. In this work, we address this gap by benchmarking a carefully selected variety of existing security metrics in terms of their usability for CPS. Accordingly, we pinpoint critical CPS challenges and qualitatively assess the effectiveness of the existing metrics for CPS systems.

Measuring software complexity is key in managing the software lifecycle and in controlling its maintenance. While there are well-established and comprehensive metrics to measure the complexity of the software code, assessment of the complexity of software designs remains elusive. Moreover, there are no clear guidelines to help software designers chose alternatives that reduce design complexity, improve design comprehensibility, and improve the maintainability of the software. This paper outlines a language independent approach to measuring software design complexity using objective and deterministic metrics. The paper outlines the metrics for two major software design notations; UML Class Diagrams and UML State Machines. The approach is based on the analysis of the design elements and their mutual interactions. The approach can be extended to cover other UML design notations.

The emergence of Industrial Cyber-Physical Systems (ICPS) in today's business world is still steadily progressing to new dimensions. Although they bring many new advantages to business processes and enable automation and a wider range of service capability, they also propose a variety of new challenges. One major challenge, which is introduced by such System-of-Systems (SoS), lies in the security aspect. As security may not have had that significant role in traditional embedded system engineering, a generic way to measure the level of security within an ICPS would provide a significant benefit for system engineers and involved stakeholders. Even though many security metrics and frameworks exist, most of them insufficiently consider an SoS context and the challenges of such environments. Therefore, we aim to define a security metric for ICPS, which measures the level of security during the system design, tests, and integration as well as at runtime. For this, we try to focus on a semantic point of view, which on one hand has not been considered in security metric definitions yet, and on the other hand allows us to handle the complexity of SoS architectures. Furthermore, our approach allows combining the critical characteristics of an ICPS, like uncertainty, required reliability, multi-criticality and safety aspects.

Accessing the secured data through the network is a major task in emerging technology. Data needs to be protected from the network vulnerabilities, malicious users, hackers, sniffers, intruders. The novel framework has been designed to provide high security in data transaction through computer network. The implant of network amalgamation in the recent trends, make the way in security enhancement in an efficient manner through the machine learning algorithm. In this system the usage of the biometric authenticity plays a vital role for unique approach. The novel mathematical approach is used in machine learning algorithms to solve these problems and provide the security enhancement. The result shows that the novel method has consistent improvement in enhancing the security of data transactions in the emerging technologies.

Implementations of Cyber-Physical Systems (CPS), like the Internet of Things, Smart Factories or Smart Grid gain more and more impact in their fields of application, as they extend the functionality and quality of the offered services significantly. However, the coupling of safety-critical embedded systems and services of the cyber-space domain introduce many new challenges for system engineers. Especially, the goal to achieve a high level of security throughout CPS presents a major challenge. However, it is necessary to develop and deploy secure CPS, as vulnerabilities and threats may lead to a non- or maliciously modified functionality of the CPS. This could ultimately cause harm to life of involved actors, or at least sensitive information can be leaked or lost. Therefore, it is essential that system engineers are aware of the level of security of the deployed CPS. For this purpose, security metrics and security evaluation frameworks can be utilized, as they are able to quantitatively express security, based on different measurements and rules. However, existing security scoring solutions may not be able to generate accurate security scores for CPS, as they insufficiently consider the typical CPS characteristics, like the communication of heterogeneous systems of physical- and cyber-space domain in an unpredictable manner. Therefore, we propose a security analysis framework, called Security Qualification Matrix (SQM). The SQM is capable to analyses multiple attacks on a System-of-Systems level simultaneously. With this approach, dependencies, potential side effects and the impact of mitigation concepts can quickly be identified and evaluated.

Cloud Storage Brokers (CSB) provide seamless and concurrent access to multiple Cloud Storage Services (CSS) while abstracting cloud complexities from end-users. However, this multi-cloud strategy faces several security challenges including enlarged attack surfaces, malicious insider threats, security complexities due to integration of disparate components and API interoperability issues. Novel security approaches are imperative to tackle these security issues. Therefore, this paper proposes CS-BAuditor, a novel cloud security system that continuously audits CSB resources, to detect malicious activities and unauthorized changes e.g. bucket policy misconfigurations, and remediates these anomalies. The cloud state is maintained via a continuous snapshotting mechanism thereby ensuring fault tolerance. We adopt the principles of chaos engineering by integrating BrokerMonkey, a component that continuously injects failure into our reference CSB system, CloudRAID. Hence, CSBAuditor is continuously tested for efficiency i.e. its ability to detect the changes injected by BrokerMonkey. CSBAuditor employs security metrics for risk analysis by computing severity scores for detected vulnerabilities using the Common Configuration Scoring System, thereby overcoming the limitation of insufficient security metrics in existing cloud auditing schemes. CSBAuditor has been tested using various strategies including chaos engineering failure injection strategies. Our experimental evaluation validates the efficiency of our approach against the aforementioned security issues with a detection and recovery rate of over 96 %.