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  • 101.
    Yalew, Sileshi Demesie
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Mobile Device Security with ARM TrustZone2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Mobile devices such as smartphones are becoming the majority of computing devices due to their evolving capabilities. Currently, service providers such as nancial and healthcare institutions oer services to their clients using smartphone applications (apps). Many of these apps run on Android, the most adopted mobile operating system (OS) today. Since smartphones are designed to be carried around all the time, many persons use them to store their private data. However, the popularity of Android and the open nature of its app marketplaces make it a prime target for malware. This situation puts data stored in smartphones in jeopardy, as it can be stealthily stolen or modied by malware that infects the device.

    With the increasing popularity of smartphones and the increasing amount of personal data  stored on these devices, mobile device security has drawn signicant attention from both industry and academia. As a result, several security mechanisms and tools such as anti-malware software have been proposed for mobile OSs to improve the privacy of private data and to mitigate some of the security risks associated with mobile devices. However, these tools and mechanisms run in the device and assume that the mobile OS is trusted, i.e., that it is part of the trusted computing base (TCB). However, current malware often disables anti-malware software when it infects a device. For mobile phones this trend started more than a decade ago with malware such as the Metal Gear Trojan and Cabir.M, and continues to this day, e.g., with HijackRAT. In this work, we use the ARM TrustZone, a security extension for ARM processors that provides a hardware-assisted isolated environment, to implement security services that are protected from malware even if the mobile OS is compromised.

    In this thesis, we investigate two approaches to address some of the security risks associated with Android-based devices. In the rst approach, we present security services to detect intrusions in mobile devices. We design and implement services for posture assessment (which evaluates the level of trust we can have in the device), for dynamic analysis (which performs dynamic (runtime) analysis of apps using traces of Android application programming interface (API) function calls and kernel syscalls to detect apps for malware), and for authenticity detection (which provides assurance of the authenticity and integrity of apps running on mobile devices). In the second approach, we design and implement a backup and recovery system to protect mobile devices from attacks caused by ransomware attacks, system errors, etc. Finally, we develop a software framework to facilitate the development of security services for mobile devices by combining components of the above services. As proof-of-concept, we implemented a prototype for each service and made experimental evaluations using an i.MX53 development board with an ARM processor with TrustZone.

  • 102.
    Zanni-Merk, Cecilia
    et al.
    Natl Inst Appl Sci Rouen Normandie, LITIS Lab, Normandy, France.;Natl Inst Appl Sci Rouen Normandie, MIND Team, Normandy, France..
    Frydman, Claudia
    Aix Marseille Univ, Marseille, France..
    Håkansson, Anne
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Special Issue: Advances in Knowledge-Based and Intelligent Engineering and Information Systems Preface2018In: DATA TECHNOLOGIES AND APPLICATIONS, ISSN 2514-9288, Vol. 52, no 4, p. 462-462Article in journal (Other academic)
  • 103.
    Zeng, Jingna
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. INESC-ID/Instituto Superior Tecnico, Universidade de Lisboa, Portugal.
    Romano, P.
    Barreto, J.
    Rodrigues, L.
    Haridi, S.
    Online tuning of parallelism degree in parallel nesting transactional memory2018In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 474-483, article id 8425201Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of self-Tuning the parallelism degree in Transactional Memory (TM) systems that support parallel nesting (PN-TM). This problem has been long investigated for TMs not supporting nesting, but, to the best of our knowledge, has never been studied in the context of PN-TMs. Indeed, the problem complexity is inherently exacerbated in PN-TMs, since these require to identify the optimal parallelism degree not only for top-level transactions but also for nested sub-Transactions. The increase of the problem dimensionality raises new challenges (e.g., increase of the search space, and proneness to suffer from local maxima), which are unsatisfactorily addressed by self-Tuning solutions conceived for flat nesting TMs. We tackle these challenges by proposing AUTOPN, an on-line self-Tuning system that combines model-driven learning techniques with localized search heuristics in order to pursue a twofold goal: i) enhance convergence speed by identifying the most promising region of the search space via model-driven techniques, while ii) increasing robustness against modeling errors, via a final local search phase aimed at refining the model's prediction. We further address the problem of tuning the duration of the monitoring windows used to collect feedback on the system's performance, by introducing novel, domain-specific, mechanisms aimed to strike an optimal trade-off between latency and accuracy of the self-Tuning process. We integrated AUTOPN with a state of the art PN-TM (JVSTM) and evaluated it via an extensive experimental study. The results of this study highlight that AUTOPN can achieve gains of up to 45× in terms of increased accuracy and 4× faster convergence speed, when compared with several on-line optimization techniques (gradient descent, simulated annealing and genetic algorithm), some of which were already successfully used in the context of flat nesting TMs.

  • 104.
    Åkerblom, Beatrice
    et al.
    Computer and Systems Science Stockholm University.
    Castegren, Elias
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Wrigstad, Tobias
    Uppsala University.
    Progress Report: Exploring API Design for Capabilities for Programming with Arrays2019In: ECOOP 2019, 2019Conference paper (Refereed)
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