Biblio

Continuous monitoring and risk assessment of privacy violations on cloud systems are needed by anyone who has business needs subject to privacy regulations. Compliance to such regulations in dynamic systems demands appropriate techniques, tools and instruments. As a Service concepts can be a good option to support this task. Previous work presented PRIVAaaS, a software toolkit that allows controlling and reducing data leakages, thus preserving privacy, by providing anonymization capabilities to query-based systems. This short paper discusses the implementation details and deployment environment of an evolution of PRIVAaaS as a MAPE-K control loop within the ATMOSPHERE Platform. ATMOSPHERE is both a framework and a platform enabling the implementation of trustworthy cloud services. By enabling PRIVAaaS within ATMOSPHERE, privacy is made one of several trustworthiness properties continuously monitored and assessed by the platform with a software-based, feedback control loop known as MAPE-K.

Nowadays, the domain of Information System (IS) security is closely related to that of Risk Management (RM). As an immediate consequence, talking about and tackling the security of IS imply the implementation of a set of mechanisms that aim to reduce or eliminate the risk of IS degradations. Also, the high cadence of IS evolution requires careful consideration of corresponding measures to prevent or mitigate security risks that may cause the degradation of these systems. From this perspective, an access control service is subjected to a number of rules established to ensure the integrity and confidentiality of the handled data. During their lifecycle, the use or manipulation of Access Control Policies (ACP) is accompanied with several defects that are made intentionally or not. For many years, these defects have been the subject of numerous studies either for their detection or for the analysis of the risks incurred by IS to their recurrence and complexity. In our research works, we focus on the analysis and risk assessment of noncompliance anomalies in concrete instances of access control policies. We complete our analysis by studying and assessing the risks associated with the correlation that may exist between different anomalies. Indeed, taking into account possible correlations can make a significant contribution to the reliability of IS. Identifying correlation links between anomalies in concrete instances of ACP contributes in discovering or detecting new scenarios of alterations and attacks. Therefore, once done, this study mainly contributes in the improvement of our risk assessment model.

Operating data analysis means to analyze the operating system logs, user operation logs, various types of alarms and security relevant configurations, etc. The purpose is to find whether there is an attack event, suspicious behaviors or improper configurations. It is an important part of risk assessment for enterprise intranet. However, due to the lack of information security knowledge or relevant experience, many people do not know how to properly implement it. In this article, we provided guidance on conducting operating data analysis and how to determine the security risk with the analysis results.

Risk assessment of cyber-physical systems, such as power plants, connected devices and IT-infrastructures has always been challenging: safety (i.e., absence of unintentional failures) and security (i. e., no disruptions due to attackers) are conditions that must be guaranteed. One of the traditional tools used to help considering these problems is attack trees, a tree-based formalism inspired by fault trees, a well-known formalism used in safety engineering. In this paper we define and implement the translation of attack-fault trees (AFTs) to a new extension of timed automata, called parametric weighted timed automata. This allows us to parametrize constants such as time and discrete costs in an AFT and then, using the model-checker IMITATOR, to compute the set of parameter values such that a successful attack is possible. Using the different sets of parameter values computed, different attack and fault scenarios can be deduced depending on the budget, time or computation power of the attacker, providing helpful data to select the most efficient counter-measure.

In this paper, we propose a game-attack graph-based risk assessment model for industrial Internet system. Firstly, use non-destructive asset profiling to scan components and devices included in the system and their open services and communication protocols. Further compare the CNVD and CVE to find the vulnerability through the search engine keyword segment matching method, and generate an asset threat list. Secondly, build the attack rule base based on the network information, and model the system using the attribute attack graph. Thirdly, combine the game theory with the idea of the established model. Finally, optimize and quantify the analysis to get the best attack path and the best defense strategy.

This paper discusses two pieces of software designed for intelligence analysis, the brainstorming tool and the Scenario Planning Advisor. These tools were developed in the Cognitive Immersive Systems Lab (CISL) in conjunction with IBM. We discuss the immersive environment the tools are situated in, and the proposed benefit for intelligence analysis.

The most significant threats to networks usually originate from external entities. As such, the Northbound interface of SDN networks which ensures communication with external applications requires particularly close attention. In this paper we propose the Risk Assessment and Management approach to SEcure SDN (RAMSES). This novel solution is able to estimate the risk associated with traffic demand requests received via the Northbound-API in SDN networks. RAMSES quantifies the impact on network cost incurred by expected traffic demands and specifies the likelihood of adverse requests estimated using the reputation system. Accurate risk estimation allows SDN network administrators to make the right decisions and mitigate potential threat scenarios. This can be observed using extensive numerical verification based on an network optimization tool and several scenarios related to the reputation of the sender of the request. The verification of RAMSES confirmed the usefulness of its risk assessment approach to protecting SDN networks against threats associated with the Northbound-API.

Regulatory efforts such as the General Data Protection Regulation (GDPR) embody a notion of privacy risk that is centered around the fundamental rights of data subjects. This is, however, a fundamentally different notion of privacy risk than the one commonly used in threat modeling which is largely agnostic of involved data subjects. This mismatch hampers the applicability of privacy threat modeling approaches such as LINDDUN in a Data Protection by Design (DPbD) context. In this paper, we present a data subject-aware privacy risk assessment model in specific support of privacy threat modeling activities. This model allows the threat modeler to draw upon a more holistic understanding of privacy risk while assessing the relevance of specific privacy threats to the system under design. Additionally, we propose a number of improvements to privacy threat modeling, such as enriching Data Flow Diagram (DFD) system models with appropriate risk inputs (e.g., information on data types and involved data subjects). Incorporation of these risk inputs in DFDs, in combination with a risk estimation approach using Monte Carlo simulations, leads to a more comprehensive assessment of privacy risk. The proposed risk model has been integrated in threat modeling tool prototype and validated in the context of a realistic eHealth application.

Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.

We address the need for security requirements to take into account risks arising from complex supply chains underpinning cyber-physical infrastructures such as industrial control systems (ICS). We present SEISMiC (SEcurity Industrial control SysteM supply Chains), a framework that takes into account the whole spectrum of security risks - from technical aspects through to human and organizational issues - across an ICS supply chain. We demonstrate the effectiveness of SEISMiC through a supply chain risk assessment of Natanz, Iran's nuclear facility that was the subject of the Stuxnet attack.

Several operational and economic factors impact the patching decisions of critical infrastructures. The constraints imposed by such factors could prevent organizations from fully remedying all of the vulnerabilities that expose their (critical) assets to risk. Therefore, an involved decision maker (e.g. security officer) has to strategically decide on the allocation of possible remediation efforts towards minimizing the inherent security risk. This, however, involves the use of comparative judgments to prioritize risks and remediation actions. Throughout this work, the security risk is quantified using the security metric Time-To-Compromise (TTC). Our main contribution is to provide a generic TTC estimator to comparatively assess the security posture of computer networks taking into account interdependencies between the network components, different adversary skill levels, and characteristics of (known and zero-day) vulnerabilities. The presented estimator relies on a stochastic TTC model and Monte Carlo simulation (MCS) techniques to account for the input data variability and inherent prediction uncertainties.

Despite more than a decade of heightened focus on cybersecurity, cyber threats remain an ongoing and growing concern [1]-[3]. Stakeholders often perform cyber risk assessments in order to understand potential mission impacts due to cyber threats. One common approach to cyber risk assessment is event-based analysis which usually considers adverse events, effects, and paths through a system, then estimates the effort/likelihood and mission impact of such attacks. When conducted manually, this type of approach is labor-intensive, subjective, and does not scale well to complex systems. As an alternative, we present an automated capability-based risk assessment approach, compare it to manual event-based analysis approaches, describe its application to a notional space system ground segment, and discuss the results.

In the computer based solutions of the problems in today's world; if the problem has a high complexity value, different requirements can be addressed such as necessity of simultaneous operation of many computers, the long processing times for the operation of algorithms, and computers with hardware features that can provide high performance. For this reason, it is inevitable to use a computer based on quantum physics in the near future in order to make today's cryptosystems unsafe, search the servers and other information storage centers on internet very quickly, solve optimization problems in the NP-hard category with a very wide solution space and analyze information on large-scale data processing and to process high-resolution image for artificial intelligence applications. In this study, an examination of quantum approaches and quantum computers, which will be widely used in the near future, was carried out and the areas in which such innovation can be used was evaluated. Malicious or non-malicious use of quantum computers with this capacity, the advantages and disadvantages of the high performance which it provides were examined under the head of security, the effect of this recent technology on the existing security systems was investigated.

Connected and automated vehicles aim to improve the comfort and the safety of the driver and passengers. To this end, car manufacturers continually improve actual standardized methods to ensure their customers safety, privacy, and vehicles security. However, these methods do not support fully autonomous vehicles, linkability and confusion threats. To address such gaps, we propose a systematic threat analysis and risk assessment framework, SARA, which comprises an improved threat model, a new attack method/asset map, the involvement of the attacker in the attack tree, and a new driving system observation metric. Finally, we demonstrate its feasibility in assessing risk with two use cases: Vehicle Tracking and Comfortable Emergency Brake Failure.

The security of clients' applications on the cloud platforms has been of great interest. Security concerns associated with cloud computing are improving in both the domains; security issues faced by cloud providers and security issues faced by clients. However, security concerns still remain in domains like cloud auditing and migrating application components to cloud to make the process more secure and cost-efficient. To an extent, this can be attributed to a lack of detailed information being publicly present about the cloud platforms and their security policies. A resolution in this regard can be found in Cloud Security Alliance's Security, Trust, and Assurance Registry (STAR) which documents the security controls provided by popular cloud computing offerings. In this paper, we perform some descriptive analysis on STAR data in an attempt to comprehend the information publicly presented by different cloud providers. It is to help clients in more effectively searching and analyzing the required security information they need for the decision making process for hosting their applications on cloud. Based on the analysis, we outline some augmentations that can be made to STAR as well as certain specific design improvements for a cloud migration risk assessment framework.

Cyber situational awareness has become increasingly important for proactive risk management to help detect and mitigate cyber attacks. Being aware of the importance of individual information system assets to the goal or mission of the organisation is critical to help minimise enterprise risk. However current risk assessment methodologies do not give explicit support to assess mission related asset criticality. This paper describes ongoing efforts within the H2020 PROTECTIVE project to define a practical mission-centric risk assessment methodology for use across diverse organisation types.

The alarming rate of big data usage in the cloud makes data exposed easily. Cloud which consists of many servers linked to each other is used for data storage. Having owned by third parties, the security of the cloud needs to be looked at. Risks of storing data in cloud need to be checked further on the severity level. There should be a way to access the risks. Thus, the objective of this paper is to use SLR so that we can have extensive background of literatures on risk assessment for big data in cloud computing environment from the perspective of security, privacy and trust.

With the advent of network connectivity and complex software applications, life-critical systems like medical devices are subject to a plethora of security risks and vulnerabilities. Security threats and attacks exploiting these vulnerabilities have been shown to compromise patient safety by hampering essential functionality. This necessitates incorporating security from the very design of software. Isolation of software functionality into different modes and switching between them based on risk assessment, while maintaining a fail-safe mode ensuring device's essential functionality is a compelling design. Formal modeling is an essential ingredient for verification of such a design. Hence, in this paper, we formally model a trustworthy multi-modal framework for life-critical systems security and in turn safety. We formalize a multiple mode based software design approach of operation with a fail-safe mode that maintains critical functionality. We ensure trustworthyness by formalizing a composite risk model incorporated into the design for run-time risk assessment and management.

Risk Assessment is a foundation of decision-making about a future project behaviour or action. The related decision made might entail further analyzes to perform risk- reduction. The risk is a general phenomenon that takes different depicts and types. Static risk and its circumstances do not significantly change over time while dynamic risk arises out of the changes in interrelated circumstances. A Multi-Agent System (MAS) approach has become a popular tool to tackle different problems that relate to risk. The MAS helps in the decision aid processes and when responding to the consequences of the risk. This paper surveys some of the existing methods and techniques of risk assessment in different application domains. The survey focuses on the employment of MAS approach in risk assessment. The survey outcomes an illustration of the roles and contributions of the MAS in the Dynamic Risk Assessment (DRA) field.

In order to evaluate the network security risks and implement effective defenses in industrial control system, a risk assessment method for industrial control systems based on attack graphs is proposed. Use the concept of network security elements to translate network attacks into network state migration problems and build an industrial control network attack graph model. In view of the current subjective evaluation of expert experience, the atomic attack probability assignment method and the CVSS evaluation system were introduced to evaluate the security status of the industrial control system. Finally, taking the centralized control system of the thermal power plant as the experimental background, the case analysis is performed. The experimental results show that the method can comprehensively analyze the potential safety hazards in the industrial control system and provide basis for the safety management personnel to take effective defense measures.

Traceability has grown from being a specialized need for certain safety critical segments of the industry, to now being a recognized value-add tool for the industry as a whole that can be utilized for manual to automated processes End to End throughout the supply chain. The perception of traceability data collection persists as being a burden that provides value only when the most rare and disastrous of events take place. Disparate standards have evolved in the industry, mainly dictated by large OEM companies in the market create confusion, as a multitude of requirements and definitions proliferate. The intent of the IPC-1782 project is to bring the whole principle of traceability up to date and enable business to move faster, increase revenue, increase productivity, and decrease costs as a result of increased trust. Traceability, as defined in this standard will represent the most effective quality tool available, becoming an intrinsic part of best practice operations, with the encouragement of automated data collection from existing manufacturing systems which works well with Industry 4.0, integrating quality, reliability, product safety, predictive (routine, preventative, and corrective) maintenance, throughput, manufacturing, engineering and supply-chain data, reducing cost of ownership as well as ensuring timeliness and accuracy all the way from a finished product back through to the initial materials and granular attributes about the processes along the way. The goal of this standard is to create a single expandable and extendable data structure that can be adopted for all levels of traceability and enable easily exchanged information, as appropriate, across many industries. The scope includes support for the most demanding instances for detail and integrity such as those required by critical safety systems, all the way through to situations where only basic traceability, such as for simple consumer products, are required. A key driver for the adoption of the standard is the ability to find a relevant and achievable level of traceability that exactly meets the requirement following risk assessment of the business. The wealth of data accessible from traceability for analysis (e.g.; Big Data, etc.) can easily and quickly yield information that can raise expectations of very significant quality and performance improvements, as well as providing the necessary protection against the costs of issues in the market and providing very timely information to regulatory bodies along with consumers/customers as appropriate. This information can also be used to quickly raise yields, drive product innovation that resonates with consumers, and help drive development tests & design requirements that are meaningful to the Marketplace. Leveraging IPC 1782 to create the best value of Component Traceability for your business.

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.

The paper suggests several techniques for computer network risk assessment based on Common Vulnerability Scoring System (CVSS) and attack modeling. Techniques use a set of integrated security metrics and consider input data from security information and event management (SIEM) systems. Risk assessment techniques differ according to the used input data. They allow to get risk assessment considering requirements to the accuracy and efficiency. Input data includes network characteristics, attacks, attacker characteristics, security events and countermeasures. The tool that implements these techniques is presented. Experiments demonstrate operation of the techniques for different security situations.

Quantitative risk assessment is a critical first step in risk management and assured design of networked computer systems. It is challenging to evaluate the marginal probabilities of target states/conditions when using a probabilistic attack graph to represent all possible attack paths and the probabilistic cause-consequence relations among nodes. The brute force approach has the exponential complexity and the belief propagation method gives approximation when the corresponding factor graph has cycles. To improve the approximation accuracy, a region-based method is adopted, which clusters some highly dependent nodes into regions and messages are passed among regions. Experiments are conducted to compare the performance of the different methods.

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.