10 research outputs found

    Secure Information Sharing with Distributed Ledgers

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    In 2009, blockchain technology was first introduced as the supporting database technology for digital currencies. Since then, more advanced derivations of the technology have been developed under the broader term Distributed Ledgers, with improved scalability and support for general-purpose application logic. As a distributed database, they are able to support interorganizational information sharing while assuring desirable information security attributes like non-repudiation, auditability and transparency. Based on these characteristics, researchers and practitioners alike have begun to identify a plethora of disruptive use cases for Distributed Ledgers in existing application domains. While these use cases are promising significant efficiency improvements and cost reductions, practical adoption has been slow in the past years. This dissertation focuses on improving three aspects contributing to slow adoption. First, it attempts to identify application areas and substantiated use cases where Distributed Ledgers can considerably advance the security of information sharing. Second, it considers the security aspects of the technology itself, identifying threats to practical applications and detection approaches for these threats. And third, it investigates success factors for successful interorganizational collaborations using Distributed Ledgers

    Trustworthiness in Mobile Cyber Physical Systems

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    Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS

    Valued Authorization Policy Existence Problem:Theory and Experiments

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    Recent work has shown that many problems of satisfiability and resiliency in workflows may be viewed as special cases of the authorization policy existence problem (APEP), which returns an authorization policy if one exists and 'No' otherwise. However, in many practical settings it would be more useful to obtain a 'least bad' policy than just a 'No', where 'least bad' is characterized by some numerical value indicating the extent to which the policy violates the base authorization relation and constraints. Accordingly, we introduce the Valued APEP, which returns an authorization policy of minimum weight, where the (non-negative) weight is determined by the constraints violated by the returned solution. We then establish a number of results concerning the parameterized complexity of Valued APEP. We prove that the problem is fixed-parameter tractable (FPT) if the set of constraints satisfies two restrictions, but is intractable if only one of these restrictions holds. (Most constraints known to be of practical use satisfy both restrictions.) We also introduce a new type of resiliency for workflow satisfiability problem, show how it can be addressed using Valued APEP and use this to build a set of benchmark instances for Valued APEP. Following a set of computational experiments with two mixed integer programming (MIP) formulations, we demonstrate that the Valued APEP formulation based on the user profile concept has FPT-like running time and usually significantly outperforms a naive formulation.Comment: 32 pages, 5 figures. Preliminary version appeared in SACMAT 2021 (https://doi.org/10.1145/3450569.3463571). Some of the theoretical results (algorithms) have been improved. Computational experiments have been added to this versio

    Resilient and Scalable Android Malware Fingerprinting and Detection

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    Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

    Collateral damage of Facebook third-party applications: a comprehensive study

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    Third-party applications on Facebook can collect personal data of the users who install them, but also of their friends. This raises serious privacy issues as these friends are not notified by the applications nor by Facebook and they have not given consent. This paper presents a detailed multi-faceted study on the collateral information collection of the applications on Facebook. To investigate the views of the users, we designed a questionnaire and collected the responses of 114 participants. The results show that participants are concerned about the collateral information collection and in particular about the lack of notification and of mechanisms to control the data collection. Based on real data, we compute the likelihood of collateral information collection affecting users: we show that the probability is significant and greater than 80% for popular applications such as TripAdvisor. We also demonstrate that a substantial amount of profile data can be collected by applications, which enables application providers to profile users. To investigate whether collateral information collection is an issue to users’ privacy we analysed the legal framework in light of the General Data Protection Regulation. We provide a detailed analysis of the entities involved and investigate which entity is accountable for the collateral information collection. To provide countermeasures, we propose a privacy dashboard extension that implements privacy scoring computations to enhance transparency toward collateral information collection. Furthermore, we discuss alternative solutions highlighting other countermeasures such as notification and access control mechanisms, cryptographic solutions and application auditing. To the best of our knowledge this is the first work that provides a detailed multi-faceted study of this problem and that analyses the threat of user profiling by application providers

    Analysis and Design of Privacy-Enhancing Information Sharing Systems

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    Recent technological advancements have enabled the collection of large amounts of personal data of individuals at an ever-increasing rate. Service providers, organisations and governments can collect or otherwise acquire rich information about individuals’ everyday lives and habits from big data-silos, enabling profiling and micro-targeting such as in political elections. Therefore, it is important to analyse systems that allow the collection and information sharing between users and to design secure and privacy enhancing solutions. This thesis contains two parts. The aim of the first part is to investigate in detail the effects of the collateral information collection of third-party applications on Facebook. The aim of the second part is to analyse in detail the security and privacy issues of car sharing systems and to design a secure and privacy-preserving solution. In the first part, we present a detailed multi-faceted study on the collateral information collection privacy issues of Facebook applications; providers of third-party applications on Facebook exploit the interdependency between users and their friends. The goal is to (i) study the existence of the problem, (ii) investigate whether Facebook users are concerned about the issue, quantify its (iii) likelihood and (iv) impact of collateral information collection affecting users, (v) identify whether collateral information collection is an issue for the protection of the personal data of Facebook users under the legal framework, and (vi) we propose solutions that aim to solve the problem of collateral information collection. In order to investigate the views of the users, we designed a questionnaire and collected the responses of participants. Employing real data from the Facebook third-party applications ecosystem, we compute the likelihood of collateral information collection affecting users and quantify its significance evaluating the amount of attributes collected by such applications. To investigate whether collateral information collection is an issue in terms of users’ privacy we analysed the legal framework in light of the General Data Protection Regulation. To provide countermeasures, we propose a privacy dashboard extension that implements privacy scoring computations to enhance transparency towards collateral information collection

    Query-based access control for secure collaborative modeling using bidirectional transformations

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    Large-scale model-driven system engineering projects are carried out collaboratively. Engineering artifacts stored in model repositories are developed in either offline (checkout-modify-commit) or online (GoogleDoc-style) scenarios. Complex systems frequently integrate models and components developed by different teams, vendors and suppliers. Thus confidentiality and integrity of design artifacts need to be protected by access control policies. We propose a technique for secure collaborative modeling where (1) fine-grained access control for models can be defined by model queries, and (2) such access control policies are strictly enforced by bidirectional model transformations. Each collaborator obtains a filtered local copy of the model containing only those model elements which they are allowed to read; write access control policies are checked on the server upon submitting model changes. We illustrate the approach and carry out an initial scalability assessment using a case study of the MONDO EU project

    The bi-objective workflow satisfiability problem and workflow resiliency

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    A computerized workflow management system may enforce a security policy, specified in terms of authorized actions and constraints, thereby restricting which users can perform particular steps in a workflow. The existence of a security policy may mean that a workflow is unsatisfiable, in the sense that it is impossible to find a valid plan (an assignment of steps to authorized users such that all constraints are satisfied). Work in the literature focuses on the workflow satisfiability problem, a decision problem that outputs a valid plan if the instance is satisfiable (and a negative result otherwise). In this paper, we introduce the Bi-Objective Workflow Satisfiability Problem (BO-WSP), which enables us to solve optimization problems related to workflows and security policies. In particular, we are able to compute a “least bad” plan when some components of the security policy may be violated. In general, BO-WSP is intractable from both the classical and parameterized complexity point of view (where the parameter is the number of steps). We prove that computing a Pareto front for BO-WSP is fixed-parameter tractable (FPT) if we restrict our attention to user-independent constraints. This result has important practical consequences, since most constraints of practical interest in the literature are user-independent. Our proof is constructive and defines an algorithm, the implementation of which we describe and evaluate. We also present a second algorithm to compute a Pareto front which solves multiples instances of a related problem using mixed integer programming (MIP). We compare the performance of both our algorithms on synthetic instances, and show that the FPT algorithm outperforms the MIP-based one by several orders of magnitude on most instances. Finally, we study the important question of workflow resiliency and prove new results establishing that known decision problems are fixed-parameter tractable when restricted to user-independent constraints. We then propose a new way of modeling the availability of users and demonstrate that many questions related to resiliency in the context of this new model may be reduced to instances of BO-WSP
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