This paper describes a Zero Trust Architecture (ZTA) approach for the survivability development of mission critical embedded systems. Designers could use ZTA as a systems analysis tool to explore the design space. The ZTA concept of “never trust, always verify” is being leveraged in the design process to guide the selection of security and resilience features for the codesign of functionality, performance, and survivability. The design example of a small drone for survivability is described along with the explanation of the ZTA approach.

Cybersecurity is largely based on the use of frameworks (ISO27k, NIST, etc.) which main objective is compliance with the standard. They do not, however, address the quantification of the risk deriving from a threat scenario. This paper proposes a methodology that, having evaluated the overall capability of the controls of an ISO27001 framework, allows to select those that mitigate a threat scenario and evaluate the risk according to a Cybersecurity Risk Quantification model.

Simulation research on fish schooling behavior is of great significance. This paper proposes an improved fish schooling behavior simulation model, which introduces fish collision avoidance, escape, and pursuit rules based on the Boids model, so that the model can simulate the response of fish when facing threats. And the simulation of fish schooling behavior in complex environment was present based on Unity3D. The quantitative analysis of the simulation results shows that the model proposed in this paper can effectively reflect the behavior al characteristics of fish schools. These results are highly consistent with the actual fish schooling behavior, which clearly demonstrates the feasibility of the model in simulating fish schooling behavior.

Cyber-physical system such as automatic metering infrastructure (AMI) are overly complex infrastructures. With myriad stakeholders, real-time constraints, heterogeneous platforms and component dependencies, a plethora of attacks possibilities arise. Despite the best of available technology countermeasures and compliance standards, security practitioners struggle to protect their infrastructures. At the same time, it is important to note that not all attacks are same in terms of their likelihood of occurrence and impact. Hence, it is important to rank the various attacks and perform scenario analysis to have an objective decision on security countermeasures. In this paper, we make a comprehensive security risk analysis of AMI, both qualitatively and quantitatively. Qualitative analysis is performed by ranking the attacks in terms of sensitivity and criticality. Quantitative analysis is done by arranging the attacks as an attack tree and performing Bayesian analysis. Typically, state-of–the-art quantitative security risk analysis suffers from data scarcity. We acknowledge the aforementioned problem and circumvent it by using standard vulnerability database. Different from state-of-the-art surveys on the subject, which captures the big picture, our work is geared to is provide the prioritized baselines in addressing most common and damaging attacks.

Intrusion detection is important in the defense in depth network security framework and a hot topic in computer network security in recent years. In this paper, an effective method for anomaly intrusion detection with low overhead and high efficiency is presented and applied to monitor the abnormal behavior of processes. The method is based on rough set theory and capable of extracting a set of detection rules with the minimum size to form a normal behavior model from the record of system call sequences generated during the normal execution of a process. Based on the network security knowledge base system, this paper proposes an intrusion detection model based on the network security knowledge base system, including data filtering, attack attempt analysis and situation assessment engine. In this model, evolutionary self organizing mapping is used to discover multi - target attacks of the same origin; The association rules obtained by time series analysis method are used to correlate online alarm events to identify complex attacks scattered in time; Finally, the corresponding evaluation indexes and corresponding quantitative evaluation methods are given for host level and LAN system level threats respectively. Compared with the existing IDS, this model has a more complete structure, richer knowledge available, and can more easily find cooperative attacks and effectively reduce the false positive rate.

In recent times, the research looks into the measures taken by financial institutions to secure their systems and reduce the likelihood of attacks. The study results indicate that all cultures are undergoing a digital transformation at the present time. The dawn of the Internet ushered in an era of increased sophistication in many fields. There has been a gradual but steady shift in attitude toward digital and networked computers in the business world over the past few years. Financial organizations are increasingly vulnerable to external cyberattacks due to the ease of usage and positive effects. They are also susceptible to attacks from within their own organisation. In this paper, we develop a machine learning based quantitative risk assessment model that effectively assess and minimises this risk. Quantitative risk calculation is used since it is the best way for calculating network risk. According to the study, a network s vulnerability is proportional to the number of times its threats have been exploited and the amount of damage they have caused. The simulation is used to test the model s efficacy, and the results show that the model detects threats more effectively than the other methods.

The growth of Electric Vehicles (EVs), coupled with the deployment of large-scale extreme fast charging stations (XFCSs), has increased the attack surface for EV ecosystems. To secure such critical cyber-physical systems (CPSs), it is imperative for the system defenders to perform an in-depth analysis of potential attack vectors, evaluate possible countermeasures, and analyze attack-defense scenarios quantitatively to implement a defense strategy that will provide maximum utilization of their limited resources. Therefore, a systematic framework is essential, relying on modeling tools that security experts are familiar with. In this paper, we propose a comprehensive methodology for enabling the defender to perform a high-level attack surface analysis of an XFCS and determine the defense strategy with the highest utility value. We apply STRIDE threat modeling and attack defense tree (ADT) to enumerate realizable attack paths and identify possible defense measures. We then employ analytic hierarchy process (AHP) as a multi-criteria decisionmaking algorithm to obtain the highest utility strategy for the defender to adopt. The proposed methodology is validated by demonstrating its real-world feasibility through a case study, using sample attack paths for an XFCS.

Recently, Graphical Security Models (GrSMs) became widely used for security analysis. The basic formalism called Attack Tree (AT) has been augmented with new attributes covering defence, response, and countermeasure aspects to support security modelling and analysis in vulnerable systems. Although the models have strength in visualising and analysing small attack-defence scenarios, these suffer from lack of scalability when increasing nodes and adaptability with other refinement models to show the dynamic nature and state of systems in interest. In this work, Coloured Petri net (CPN) is used to fulfil the mentioned shortcomings in GrSMs (specifically Treebased models). It is applied for evaluating each component´s interactions, the impact of threats as well as defence systems to mitigate those threats. For that end and pointing out the CPN adaptability with GrSMs, a set of mapping rules are proposed allowing translation of ATs extension into CPN and their analysis. The quantitative analysis aspect is addressed in this work by introducing computing transition. We validate our proposed approach by applying it in an example of SCADA systems cybersecurity analysis.

Cybersecurity risk analysis is crucial for orga-nizations to assess, identify, and prioritize possible threats to their systems and assets. Organizations aim to estimate the loss cost in case cybersecurity risks occur to decide the control actions they should invest in. Quantitative risk analysis aids organizations in making well-informed decisions about risk mitigation strategies and resource allocation. Therefore, organizations must use quantitative risk analysis methods to identify and prioritize risks rather than relying on qualitative methods. This paper proposes a spreadsheet-based quantitative risk analysis method based on verbal likelihoods. Our approach relies on tables constructed by experts that map between linguistic likelihood and possible probability ranges. Using linguistic terms to estimate the probability of risk occurrence will help experts apply quantitative estimation easily by using common language as input, thus eliminating the need to assign precise probabilities. We experimented with real examples to validate our approach s accuracy and reliability and compared our results with those obtained from another method. Also, we conducted tests to measure our model s performance and robustness. Our study showcases the effectiveness of our approach and demonstrates its potential for risk analysts to use it in real-world applications.

Security still remains an afterthought in modern Electronic Design Automation (EDA) tools, which solely focus on enhancing performance and reducing the chip size. Typically, the security analysis is conducted by hand, leading to vulnerabilities in the design remaining unnoticed. Security-aware EDA tools assist the designer in the identification and removal of security threats while keeping performance and area in mind. Stateof-the-art approaches utilize information flow analysis to spot unintended information leakages in design structures. However, the classification of such threats is binary, resulting in negligible leakages being listed as well. A novel quantitative analysis allows the application of a metric to determine a numeric value for a leakage. Nonetheless, current approximations to quantify the leakage are still prone to overlooking leakages. The mathematical model 2D-QModel introduced in this work aims to overcome this shortcoming. Additionally, as previous work only includes a limited threat model, multiple threat models can be applied using the provided approach. Open-source benchmarks are used to show the capabilities of 2D-QModel to identify hardware Trojans in the design while ignoring insignificant leakages.

In this paper, an air Air target threat assessment method based on a variable weight cloud Bayesian network (VWCBN) is proposed, which addresses the qualitative issue of air target threat levels, as most of the existing threat assessment results in focus on quantitative analysis. The proposed method enables high, medium, and low qualitative decision-making for air target threat levels. Firstly, a Bayesian network model that incorporates the attribute of air threat is constructed, assessing the threat level of air targets. Secondly, the cloud model is introduced to the Bayesian network, using it to represent the probability of correlation between nodes in the network. Then, by combining the battlefield situation information, using an improved variable weight method with Gaussian expression, the weights of target attributes are determined. Finally, based on the correlation probability and target attribute weight, the cloud model operation rules are utilized to obtain the decision of the air target threat level. Simulation results demonstrate that the proposed VWCBN method can effectively assess target threats, obtain air target threat level decisions, and further improve the utilization of battlefield information.

In modern conditions, the relevance of the problem of assessing the information security risks for automated systems is increasing. Risk assessment is defined as a complex multi-stage task. Risk assessment requires prompt decision-making for effective information protection. To solve this problem, a method for automating risk assessment based on fuzzy cognitive maps is proposed. A fuzzy cognitive map is a model that can be represented as a directed graph in which concepts and connections between them have own weights. The automation process allows evaluate complex relationships between factors and threats, providing a more comprehensive risk assessment. The application of fuzzy cognitive maps proved to be an effective tool for automation, promptness, and quality in risk assessment.

The role of principals of schools facing digital transformation in and for the 21st century is to assure and promote effective use of digital technologies in all aspects of school functioning.

The increase in the usage of various computing and mobile devices has resulted in implementing large scale ad hoc networks as the user demand is on the rise and companies’ find it difficult to invest more in the IT infrastructure to meet the surging demand. The traditional model of networking enables the mobile devices to face various issues like lower bandwidth, mobility, security and storage et. Hence, in order to meet the overall service requirement and to enhance the overall efficiency of the network, cloud computing was introduced. The implementation of these devices tends to support in every node, it enhances better communication in a better range towards another nodes. There is a critical administration and support devices from everywhere in an effective manner.

In this paper, we present a novel statistical approach to assess and model data of water distribution network (WDN) failures which contain only few pieces of information, namely the number of failures in a month. The applied statistical method is known as the circular (directional) statistics. It concerns with angular/cyclical data in degrees or radians. The sample space is typically a circle or a sphere and due to the nature of the circular data, they cannot be analysed with commonly used statistical techniques. Circular data approaches can be adapted to analyse time-of-year data and year cycles. Using the methods of descriptive and inferential statistics for circular data, we show that the WDN failure data show a deviation from the uniform model and cannot be modelled by the parametric models. Therefore, we apply the nonparametric circular kernel density estimates to assess and model the data and predict the expected numbers of failures in the respective months of a year.

The growth of Electric Vehicles (EVs), coupled with the deployment of large-scale extreme fast charging stations (XFCSs), has increased the attack surface for EV ecosystems. To secure such critical cyber-physical systems (CPSs), it is imperative for the system defenders to perform an in-depth analysis of potential attack vectors, evaluate possible countermeasures, and analyze attack-defense scenarios quantitatively to implement a defense strategy that will provide maximum utilization of their limited resources. Therefore, a systematic framework is essential, relying on modeling tools that security experts are familiar with. In this paper, we propose a comprehensive methodology for enabling the defender to perform a high-level attack surface analysis of an XFCS and determine the defense strategy with the highest utility value. We apply STRIDE threat modeling and attack defense tree (ADT) to enumerate realizable attack paths and identify possible defense measures. We then employ analytic hierarchy process (AHP) as a multi-criteria decisionmaking algorithm to obtain the highest utility strategy for the defender to adopt. The proposed methodology is validated by demonstrating its real-world feasibility through a case study, using sample attack paths for an XFCS.

With technological advances, Cyber-Physical Systems (CPS), specifically critical infrastructures, have become strongly connected. Their exposure to cyber adversaries is higher than ever. The number of cyber-attacks perpetrated against critical infrastructure is growing in number and sophistication. The protection of such complex systems became of paramount importance. Resilience applied to critical infrastructures aims at protecting these vital systems from cyber-attacks and making them continue to deliver a certain level of performance, even when attacks occur. In this work, we explore new advances related to cyber-resilience applied to CPSs. We also explore the use of a metric to quantify the resilience of critical infrastructures. As a use case, we consider a water treatment system.

Modern day cyber-infrastructures are critically dependent on each other to provide essential services. Current frameworks typically focus on the risk analysis of an isolated infrastructure. Evaluation of potential disruptions taking the heterogeneous cyber-infrastructures is vital to note the cascading disruption vectors and determine the appropriate interventions to limit the damaging impact. This paper presents a cyber-security risk assessment framework for the interconnected cyberinfrastructures. Our methodology is designed to be comprehensive in terms of accommodating accidental incidents and malicious cyber threats. Technically, we model the functional dependencies between the different architectures using reliability block diagrams (RBDs). RBDs are convenient, yet powerful graphical diagrams, which succinctly describe the functional dependence between the system components. The analysis begins by selecting a service from the many services that are outputted by the synchronized operation of the architectures whose disruption is deemed critical. For this service, we design an attack fault tree (AFT). AFT is a recent graphical formalism that combines the two popular formalisms of attack trees and fault trees. We quantify the attack-fault tree and compute the risk metrics – the probability of a disruption and the damaging impact. For this purpose, we utilize the open source ADTool. We show the efficacy of our framework with an example outage incident.

In this paper, a quantitative analysis method is proposed to calculate the risks from cyber-attacks focused on the domain of data security in the financial sector. Cybersecurity risks have increased in organizations due to the process of digital transformation they are going through, reflecting in a notorious way in the financial sector, where a considerable percentage of the attacks carried out on the various industries are concentrated. In this sense, risk assessment becomes a critical point for their proper management and, in particular, for organizations to have a risk analysis method that allows them to make cost-effective decisions. The proposed method integrates a layered architecture, a list of attacks to be prioritized, and a loss taxonomy to streamline risk analysis over the data security domain including: encryption, masking, deletion, and resiliency. The layered architecture considers: presentation layer, business logic layer, and data management layer. The method was validated and tested by 6 financial companies in Lima, Peru. The preliminary results identified the applicability of the proposed method collected through surveys of experts from the 6 entities surveyed, obtaining 85.7\% who consider that the proposed three-layer architecture contains the assets considered critical.

For modern industrial automation and control systems (IACS), it is a cybersecurity risk that provokes the most growing anxiety among other potential hazards. In order to manage the risk efficiently, a risk assessment is necessary. A standard CIA approach explores the confidentiality, integrity, and availability properties of assets. However, for IACS dealing with critical infrastructures, it is more crucial to investigate separately the availability part of the risk. Moreover, not assets but functions are particularly important. One of the major problems arising during the assessment is how to assign values for the availability property of IACS functions. For establishing the CIA values, techniques related to confidentiality and integrity seem to be quite evident and make just a minor issue to develop and employ. However, methods for assessing the availability property happen to be not obvious and not widely used. The article presents a metric helpful for the availability valuation. Inherently constructed to be applicable particularly to functions, not to assets, the metric will be found especially effective for IACS. Essentially based on delay as a measure, the metric is proved to be conformant to the IEC 62443 series availability interpretation and the general requirements for the cybersecurity metrics. For the metric to be accurately calculated, the availability reference model, dependency theory, and a theory of deterministic queuing systems Network calculus are proposed to be utilized. Applying Network calculus to the metric calculation, the article reveals that this problem can be reduced to the problem of obtaining sets of service curves.

Cyber physical system (CPS) Critical infrastructures (CIs) like the power and energy systems are increasingly becoming vulnerable to cyber attacks. Mitigating cyber risks in CIs is one of the key objectives of the design and maintenance of these systems. These CPS CIs commonly use legacy devices for remote monitoring and control where complete upgrades are uneconomical and infeasible. Therefore, risk assessment plays an important role in systematically enumerating and selectively securing vulnerable or high-risk assets through optimal investments in the cybersecurity of the CPS CIs. In this paper, we propose a CPS CI security framework and software tool, CySec Game, to be used by the CI industry and academic researchers to assess cyber risks and to optimally allocate cybersecurity investments to mitigate the risks. This framework uses attack tree, attackdefense tree, and game theory algorithms to identify high-risk targets and suggest optimal investments to mitigate the identified risks. We evaluate the efficacy of the framework using the tool by implementing a smart grid case study that shows accurate analysis and feasible implementation of the framework and the tool in this CPS CI environment.

Due to the concern on cloud security, digital encryption is applied before outsourcing data to the cloud for utilization. This introduces a challenge about how to efficiently perform queries over ciphertexts. Crypto-based solutions currently suffer from limited operation support, high computational complexity, weak generality, and poor verifiability. An alternative method that utilizes hardware-assisted Trusted Execution Environment (TEE), i.e., Intel SGX, has emerged to offer high computational efficiency, generality and flexibility. However, SGX-based solutions lack support on multi-user query control and suffer from security compromises caused by untrustworthy TEE function invocation, e.g., key revocation failure, incorrect query results, and sensitive information leakage. In this article, we leverage SGX and propose a secure and efficient SQL-style query framework named QShield. Notably, we propose a novel lightweight secret sharing scheme in QShield to enable multi-user query control; it effectively circumvents key revocation and avoids cumbersome remote attestation for authentication. We further embed a trust-proof mechanism into QShield to guarantee the trustworthiness of TEE function invocation; it ensures the correctness of query results and alleviates side-channel attacks. Through formal security analysis, proof-of-concept implementation and performance evaluation, we show that QShield can securely query over outsourced data with high efficiency and scalable multi-user support.

One of the important characteristics envisioned for 6G is security function virtualization (SFV). Similar to network function virtualization (NFV) in 5G networks, SFV provides new opportunities for improving security while reducing the security overhead. In particular, it provides an attractive way of solving compatibility issues related to security. Malware in Internet of Things (IoT) systems is gaining popularity among cyber-criminals because of the expected number of IoT devices in 5G and 6G networks. To solve this issue, this article proposes a security framework that exploits softwarization of security functions via SFV to improve trust in IoT systems and contain the propagation of malware. IoT devices are categorized into trusted, vulnerable, and compromised levels using remote attestation. To isolate the devices in the three distinct categories, NFV is used to create separate networks for each category, and a distributed ledger is used to store the state of each device. Virtualized remote attestation routines are employed to avoid any compatibility issues among heterogeneous IoT devices and effectively contain malware propagation. The results show that the proposed framework can reduce the number of infected devices by 66 percent in only 10 seconds.

Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.

The releases of Intel SGX and AMD SEV mark the transition of hardware-based enclaves from research prototypes to mainstream products. These two paradigms of secure enclaves are attractive to both the cloud providers and tenants, since security is one of the key pillars of cloud computing. However, it is found that current hardware-defined enclaves are not flexible and efficient enough for the cloud. For example, although SGX can provide strong memory protection with both confidentiality and integrity, the size of secure memory is tightly restricted. On the contrary, SEV enables enclaves to use more memory but has critical security flaws due to no memory integrity protection. Meanwhile, both types of enclaves have relatively long booting latency, which makes them not suitable for short-term tasks like serverless workloads. After an in-depth analysis, we find that there are some intrinsic tradeoffs between security and performance due to the limitation of architectural designs. In this article, we investigate a novel hardware-software co-design of enclaves to meet the requirements of cloud by placing a part of the logic of the enclave mechanism into a lightweight software layer, named Enclavisor, to achieve a balance between security, performance, and flexibility. Specifically, our implementation is based on AMD’s SEV and, Enclavisor is placed in the guest kernel mode of SEV’s secure virtual machines. Enclavisor inherently supports memory encryption with no memory limitation and also achieves efficient booting, multiple enclave granularities, and post-launch remote attestation. Meanwhile, we also propose hardware/ software solutions to mitigate the security flaws caused by the lack of memory integrity. We implement a prototype of Enclavisor on an AMD SEV server. The experiments on both micro-benchmarks and application benchmarks show that enclaves on Enclavisor can have close-to-native performance.