A Probabilistic Framework for Security Scenarios with Dependent Actions

This work addresses the growing need of performing meaningful probabilistic analysis of security. We propose a framework that integrates the graphical security modeling technique of attack–defense trees with probabilistic information expressed in terms of Bayesian networks. This allows us to perform probabilistic evaluation of attack–defense scenarios involving dependent actions. To improve the efficiency of our computations, we make use of inference algorithms from Bayesian networks and encoding techniques from constraint reasoning. We discuss the algebraic theory underlying our framework and point out several generalizations which are possible thanks to the use of semiring theory

Understanding How Components of Organisations Contribute to Attacks

Attacks on organisations today explore many different layers, including buildings infrastructure, IT infrastructure, and human factor – the physical, virtual, and social layer. Identifying possible attacks, understanding their impact, and attributing their origin and contributing factors is difficult. Recently, system models have been used for automatically identifying possible attacks on the modelled organisation. The generated attacks consider all three layers, making the contribution of building infrastructure, computer infrastructure, and humans (insiders and outsiders) explicit. However, this contribution is only visible in the attack trees as part of the performed steps; it cannot be mapped back to the model directly since the actions usually involve several elements (attacker and targeted actor or asset). Especially for large attack trees, understanding the relations between several model components quickly results in a large quantity of interrelations, which are hard to grasp. In this work we present several approaches for visualising attributes of attacks such as likelihood of success, impact, and required time or skill level. The resulting visualisations provide a link between attacks on an organisations and the contribution of parts of an organisation to the attack and its impact

Attack trees in Isabelle

In this paper, we present a proof theory for attack trees. Attack trees are a well established and useful model for the construction of attacks on systems since they allow a stepwise exploration of high level attacks in application scenarios. Using the expressiveness of Higher Order Logic in Isabelle, we succeed in developing a generic theory of attack trees with a state-based semantics based on Kripke structures and CTL. The resulting framework allows mechanically supported logic analysis of the meta-theory of the proof calculus of attack trees and at the same time the developed proof theory enables application to case studies. A central correctness and completeness result proved in Isabelle establishes a connection between the notion of attack tree validity and CTL. The application is illustrated on the example of a healthcare IoT system and GDPR compliance verification

A Stochastic Framework for Quantitative Analysis of Attack-Defense Trees

Cyber attacks are becoming increasingly complex, practically sophisticated and organized. Losses due to such attacks are important, varying from the loss of money to business reputation spoilage. Therefore, there is a great need for potential victims of cyber attacks to deploy security solutions that allow the identification and/or prediction of potential cyber attacks, and deploy defenses to face them. In this paper, we propose a framework that incorporates Attack-Defense trees (ADTrees) and Continuous Time Markov Chains (CTMCs) to systematically represent attacks, defenses, and their interaction. This solution allows to perform quantitative security assessment, with an aim to predict and/or identify attacks and find the best and appropriate defenses to reduce the impact of attacks

A Stochastic Framework for Quantitative Analysis of Attack-Defense Trees

Cyber attacks are becoming increasingly complex, practically sophisticated and organized. Losses due to such attacks are important, varying from the loss of money to business reputation spoilage. Therefore, there is a great need for potential victims of cyber attacks to deploy security solutions that allow the identification and/or prediction of potential cyber attacks, and deploy defenses to face them. In this paper, we propose a framework that incorporates Attack-Defense trees (ADTrees) and Continuous Time Markov Chains (CTMCs) to systematically represent attacks, defenses, and their interaction. This solution allows to perform quantitative security assessment, with an aim to predict and/or identify attacks and find the best and appropriate defenses to reduce the impact of attacks

Psychometric Properties and Validation of the Arabic Academic Performance Rating Scale

To validate the Arabic version of Academic Performance Rating Scale. Method. Translation and test-retest reliability were computed. Exploratory factor analysis and Rasch analysis were conducted to report on the validity. EFA factor structures were evaluated using a Scree plot and the standard multiple criteria included eigenvalue greater than 1. Average measures and step measures were ordered and themean-square outfit statistic for each category was also evaluated. Results. Cronbach’s Alpha value of 0.90 was obtained. No differences across category of educational levels were seen

Using attack-defense trees to analyze threats and countermeasures in an ATM: a case study

Securing automated teller machines (ATMs), as critical and complex infrastructure, requires a precise understanding of the associated threats. This paper reports on the application of attack-defense trees to model and analyze the security of ATMs.We capture the most dangerous multi-stage attack scenarios applicable to ATM structures, and establish a practical experience report, where we re ect on the process of modeling ATM threats via attack-defense trees. In particular, we share our insights into the benets and drawbacks of attack-defense tree modeling, as well as best practices and lessons learned

Automated Model-based Attack Tree Analysis using HiP-HOPS

As Cyber-Physical Systems (CPS) grow increasingly complex and interact with external CPS, system security remains a nontrivial challenge that continues to scale accordingly, with potentially devastating consequences if left unchecked. While there is a significant body of work on system security found in industry practice, manual diagnosis of security vulnerabilities is still widely applied. Such approaches are typically resource-intensive, scale poorly and introduce additional risk due to human error. In this paper, a model-based approach for Security Attack Tree analysis using the HiP-HOPS dependability analysis tool is presented. The approach is demonstrated within the context of a simple web-based medical application to automatically generate attack trees, encapsulated as Digital Dependability Identities (DDIs), for offline security analysis. The paper goes on to present how the produced DDIs can be used to approach security maintenance, identifying security capabilities and controls to counter diagnosed vulnerabilities