Exploring risk flow attack graph for security risk assessment

Smart infrastructure integrates cyberphysical systems, self-organizing networks of devices, fog computing to enhance the quality and convenience of manufacturing and social life, providing efficient resource management and increasing productivity and availability of traditional services. New technologies are characterized with a high degree of heterogeneity, structural complexity, and dynamic topology that result in a large number of new information and cyber security threats. Given a high degree of integration of digital cyber space into the processes of management, monitoring, aggregation, and data transferring, an assessment of its security level is a necessary requirement to maintain and control a safe and reliable environment for handling data and performing physical processes. Risk analysis and assessment is an extremely important task at all stages of the life cycle of subsystems and components of the smart environment. It allows us to assess a level of protection, identify vulnerable assets, and counteract the most likely directions of security threats. The known methods for security risk assessment do not consider the features of modern smart technologies, and this forces us to search for new approaches. This paper proposes a comprehensive method that includes a calculation of security indicators, risk assessment and selection of protective measures, based on attack graphs and thus allows us to consider the dynamics of changes in the components of the smart infrastructure. The automated system for assessing the security risks in the smart infrastructure and choosing the protective measures has been implemented.

Attack graphs are the graphs which describe attack scenarios, play important roles in analyzing network threats. These attack graphs are able to reveal such potential threats by evaluating the all possible sequences that an attacker can follow to compromise given critical resources or nodes. An Attack graph specifies an attack scenario that results in compromising network values. There are so many methods proposed to evaluate the network security in attack graphs. But no method specifies the overhead occurred due to the evaluation of network security at each and every node. This paper addresses the problem of overhead occurred due to the network security evaluation in Short-Normalizedattack graphs by evaluating a factor called network security risk. In this paper first the possible n valid attack paths are going to be calculated and then the security risk is going to be calculated for those n valid paths. This security risk denotes the amount of overhead occurred due to this evaluation.

Visualization and graphical representation of a system can improve the understanding of the designer of a system and mitigate risks of attack to designed systems. An attack graphs documents the risks known at the time the system is designed. Attack graphs use graphical representation which assists in documenting security risks and identifying possible paths attackers may consider for attacking a system for their undesirable goal. However an attack graph does not provide facilities to perform concrete risk analysis such as what-if and scenarios analysis to test the designed system for possible risk of attacks. In this article, a fuzzy cognitive map (FCM) is used with graph attacks to provide facilities that will enable the system architects to perform what-if analysis to better understand vulnerabilities of their designed system.

In order to improve the accuracy of risk assessment of corporate networks, the paper introduces the concept of network shell into the field of security risk assessment. After analyzing the actual needs of corporate networks, we give the risk assessment model of network shell, then based on this model we analyze the application of network shell and give the algorithm to repair the network shell through the attack graph. After that, a calculation method of the integrated value of network shell is given. Finally, an example is given to verify the validity of assessment model.

Attack graphs contribute to the evaluation of network security vulnerabilities, offering a visualization of possible attack paths.Despite their common use in IT security for analyzing system vulnerabilities, attack graphs are not commonly used in the automotive sector.As smart vehicles increasingly rely on 5G networks for high-bandwidth, low-latency communication -necessary for advanced vehicle-to-everything (V2X) services and sensor data processing -security concerns escalate.The complexity of 5G-enabled vehicles significantly expands a vehicle's attack surface.The ISO/SAE 21434 standard establishes a framework for securing road vehicle systems.The Threat Analysis and Risk Assessment (TARA) process, a vital part of this standard, helps identify and mitigate security risks.However, the current TARA process relies heavily on manual effort to identify potential attack vectors and assess risks.This can be time consuming, resourceintensive, and prone to human error.This paper discusses the concept of an automated attack graph generation tool specifically designed for automotive threat analysis.We propose a new Graph-based Attack Path Prioritization tool (GAPP), tailored for automotive networks.GAPP focuses on generating attack paths, assessing their feasibility, and identifying the most likely attack scenarios.This aims to enhance the efficiency, comprehensiveness, and accuracy of the TARA process in evaluating network security.

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.

An intrusion detection system (IDS) generates alerts indicating what malicious behaviors are going on against the protected network system. When comparing the real-time reported IDS alerts with the network attack graph which provides all possible sequences of exploits that an intruder may use to penetrate the system, some prediction on future attacks can be made. In this paper we proposed a novel approach to predicting future attacks. First an attack graph is generated through data mining and the predictability of every attack scenario which represents how probable there would be oncoming attacks following the attack scenario can be estimated. Then in real-time intrusion detection environment the IDS alerts are correlated into attack scenarios and ranked by their predictability scores. Finally the attack scenarios with high predictability are used as the evidence to make prediction on future attacks. The effectiveness of the approach has been validated with a honeynet system.

Information security management is a managerial activity that keeps the information security risk of an enterprise at some acceptable level. The process requires risk assessment iterations and non-trivial resources. At the same time, business executives compete for finite resources to invest in general business activities. The executives are motivated to intentionally reduce the resource assignment for risk assessment to broaden the estimated risk variance and then choose the lower limit to underestimate the actual risks. This study proposes a model-based risk assessment approach; it guarantees that an additional, thus expected to be costly, fine-grained risk assessment always gives a refined subset of the possible incidents compared to that of the relatively coarse-grained, therefore cheaper, risk assessment. In other words, estimated incidents roughly decrease with the growth of assessment cost. We expect this property to secure the risk management process from conflicts between the business executives and the security practitioners. Our approach introduces the concept named aggregation and segregation of facts appearing in attack graph analysis, a prominent approach in model-based network security risk analysis. This study contributes to expanding the attack graph analysis to enable secured risk assessment.

This chapter summarizes innovations in security risk and capability assessment frameworks and methodologies to directly support dynamic decision making, forge a convergence with S&T, operations, intelligence and policy, and inform investment priorities of the Canadian Safety and Security Program (CSSP). Using a thematic approach, this paper reviews how multiple innovations were adopted as “best practices” into the conceptual design, development and implementation of security risk assessment frameworks and methodologies in the CSSP. These include the: use taxonomies to foster key stakeholder ‘buy in’; leveraging architecture frameworks to drive tools, methods and rating schema; the formulation of scenario based approaches for assessing high consequence all hazards risks; and, the application of an integrated risk and capability gap assessment model to guide investment in public safety and security S&T. In the CSSP, the innovations in security risk assessment identified best practices and common tools that supported numerous strategies and S&T portfolios (e.g., CBRNE, Critical Infrastructure Protection, all hazards safety and security; defence infrastructure/military bases), where risk assessment expertise/capability improvements that were adopted by practitioners, interdisciplinary teams and decision makers. Underpinning these innovations were strong partnerships and community-based approaches that enabled a credible risk-informed decision support environment, with consistent/scalable approaches to analysis and decision making under conditions of uncertainty.KeywordsSecurity risk assessmentVulnerability

Critical systems have very stringent requirements on both security and safety. Recent mishaps such as the missing MH370 aircraft and the sunk Korean Sewol ferry go to show that our technology in safety and security risk assessment still need a more integrated approach. Nuclear plant meltdown in the recent Fukushima accident is also a typical example of insufficient risk assessments. This work is a case study on how a unified security and safety risk assessment methodology may be applied to a High Pressure Core Flooder (HPCF) system in a nuclear power plant. Individual risk security or safety assessments may overlook the possible higher risk associated with such critical systems. The case study shows how the proposed method provides a more accurate risk assessment compared to individual assessments.

Amid the widespread use of blockchain technology, the escalating frequency of cyberattacks exploiting its inherent security challenges underscores the critical necessity for a robust and adaptable security risk assessment approach. The distinctive attributes and intricate internal structure of blockchain not only attract malicious actors but also elevate the risk of ill-informed architectural design decisions, potentially introducing security vulnerabilities. This study addresses this imperative by conducting a systematic literature review, classifying publications that elucidate secure architectural design approaches and categorising those that delineate methods for assessing security risks associated with blockchain and smart contracts. The findings reveal four prevalent approaches supporting secure architectural design—decision models, taxonomies, design patterns and guidelines—alongside contributions in blockchain risk assessment encompassing risk identification, analysis and evaluation methods. Furthermore, the study identifies unresolved architectural design challenges and proposes future research directions in this evolving landscape.

Penetration testing is an important method to evaluate the security degree of a network system. The importance of penetration testing attack path planning lies in its ability to simulate attacker behavior, identify vulnerabilities, reduce potential losses, and continuously improve security strategies. By systematically simulating various attack scenarios, it enables proactive risk assessment and the development of robust security measures. To address the problems of inaccurate path prediction and difficult convergence in the training process of attack path planning, an algorithm which combines attack graph tools (i.e., MulVAL, multi-stage vulnerability analysis language) and the double deep Q network is proposed. This algorithm first constructs an attack tree, searches paths in the attack graph, and then builds a transfer matrix based on depth-first search to obtain all reachable paths in the target system. Finally, the optimal path for target system attack path planning is obtained by using the deep double Q network (DDQN) algorithm. The MulVAL double deep Q network(MDDQN) algorithm is tested in different scale penetration testing environments. The experimental results show that, compared with the traditional deep Q network (DQN) algorithm, the MDDQN algorithm is able to reach convergence faster and more stably and improve the efficiency of attack path planning.

Nowadays, the application of enterprise management information has been deeply rooted in daily business activities, and the management risk of enterprise information security (EIS) has also increased. The use of advanced means to provide security protection for it has become the top priority. To optimize the EIS management system, and carry on the risk assessment (RA), firstly, this study analyzes the current situation of the enterprise's internal information management and summarizes the shortcomings and security risks faced by the system. In the era of big data (BD), the security risks of database information systems show a diversified trend. Secondly, to explore the application of BD technology in EIS management, the characteristics of this technology and security control measures for risks are summarized to strengthen the enterprise's information management innovation and implement the application of data security technology.

Berthing is one of the most dangerous phases in the process of ship navigation, and its risk assessment is crucial for both ship safety and port scheduling. To effectively enhance the safety and reliability of the berthing process, a berthing risk assessment method based on the synergy of “human-ship-environment” has been established. First, the impact of the human, ship, and environmental factors on berthing risk was analyzed, and a risk assessment index system for ship berthing was constructed. Then, the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods were employed for a comprehensive risk assessment. AHP was used to determine the weight of each factor reasonably, while FCE was applied for the evaluation of the berthing risk. Finally, the proposed method was applied to evaluate the berthing operations of two ships, namely the KCS ship type and the S-175 large passenger ship type, at the Qingdao Intelligent Ship Testing Field, China. The experiment’s results indicate that the evaluation results of the method proposed here have good consistency with the expert survey method.

Supply chain risk management is a newly-emerged key subject of supply chain management area. This paper classified and refined supply chain risk according to supply chain risk sources, and established a risk assessment index system. Meanwhile by using of AHP and Fuzzy Comprehensive Evaluation, which constructed risk assessment model, to quantify risk indicators. Then the supply chain risks can be calculated and that can make contribution to the supply chain risk assessment and decision-making.