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Dimitrakakis, Christos

Nom
Dimitrakakis, Christos
Affiliation principale
Fonction
Professor
Email
christos.dimitrakakis@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/30936
_
0000-0002-5367-5189

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  • Publication
    Accès libre
    VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things
    (2018)
    Luca Luceri
    ;
    Felipe Cardoso
    ;
    Michela Papandrea
    ;
    Silvia Giordano
    ;
    Julia Buwaya
    ;
    Stéphane Kundig
    ;
    Constantinos Marios Angelopoulos
    ;
    José Rolim
    ;
    Zhongliang Zhao
    ;
    Jose Luis Carrera
    ;
    Torsten Braun
    ;
    Aristide C.Y. Tossou
    ;
    Dimitrakakis, Christos 
    ;
    Aikaterini Mitrokotsa
    Smartphones are a key enabling technology in the Internet of Things (IoT) for gathering crowd-sensed data. However, collecting crowd-sensed data for research is not simple. Issues related to device heterogeneity, security, and privacy have prevented the rise of crowd-sensing platforms for scientific data collection. For this reason, we implemented VIVO, an open framework for gathering crowd-sensed Big Data for IoT services, where security and privacy are managed within the framework. VIVO introduces the enrolled crowd-sensing model, which allows the deployment of multiple simultaneous experiments on the mobile phones of volunteers. The collected data can be accessed both at the end of the experiment, as in traditional testbeds, as well as in real-time, as required by many Big Data applications. We present here the VIVO architecture, highlighting its advantages over existing solutions, and four relevant real-world applications running on top of VIVO.
  • Publication
    Accès libre
    Near-optimal blacklisting
    (2017)
    Dimitrakakis, Christos 
    ;
    Aikaterini Mitrokotsa
    Many applications involve agents sharing a resource, such as networks or services. When agents are honest, the system functions well and there is a net profit. Unfortunately, some agents may be malicious, but it may be hard to detect them. We consider the decision making problem of how to permanently blacklist agents, in order to maximise expected profit. The problem of efficiently deciding which nodes to permanently blacklist has various applications ranging from efficient intrusion response, network management, shutting down malware infected hosts in an internal network and efficient distribution of services in a network. In this paper, we propose an approach to efficiently perform this blacklisting while minimising the cost of the service provider. Although our approach is quite general and could be applied to all the previously mentioned applications, to ease understanding we consider the problem in which an Internet service provider (ISP) needs to decide whether or not to blacklist a possibly misbehaving node. This is not trivial, as blacklisting may erroneously expel honest nodes (agents). Conversely, while we gain information by allowing a node to remain, we may incur a cost due to malicious behaviour. We present an efficient algorithm (HiPER) for making near-optimal decisions for this problem. Additionally, we derive three algorithms by reducing the problem to a Markov decision process (MDP). Theoretically, we show that HiPER is near-optimal. Experimentally, its performance is close to that of the full MDP solution, when the (stronger) requirements of the latter are met.
  • Publication
    Accès libre
    Expected loss analysis for authentication in constrained channels
    (2015)
    Dimitrakakis, Christos 
    ;
    Aikaterini Mitrokotsa
    ;
    Serge Vaudenay
    We derive bounds on the expected loss for authentication protocols in channels which are constrained due to noisy conditions and communication costs. This is motivated by a number of authentication protocols, where at least some part of the authentication is performed during a phase, lasting n rounds, with no error correction. This requires assigning an acceptable threshold for the number of detected errors and taking into account the cost of incorrect authentication and of communication. This paper describes a framework enabling an expected loss analysis for all the protocols in this family. Computationally simple methods to obtain nearly optimal values for the threshold, as well as for the number of rounds are suggested and upper bounds on the expected loss, holding uniformly, are given. These bounds are tight, as shown by a matching lower bound. Finally, a method to adaptively select both the number of rounds and the threshold is proposed for a certain class of protocols.
  • Publication
    Accès libre
    Intrusion detection in MANET using classification algorithms: The effects of cost and model selection
    (2013)
    Aikaterini Mitrokotsa
    ;
    Dimitrakakis, Christos 
    Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised classification algorithms for intrusion detection on a number of metrics. We measure their performance on a dataset, described in this paper, which includes varied traffic conditions and mobility patterns for multiple attacks. One of our goals is to investigate how classification performance depends on the problem cost matrix. Consequently, we examine how the use of uniform versusweighted cost matrices affects classifier performance. A second goal is to examine techniques for tuning classifiers when unknown attack subtypes are expected during testing. Frequently, when classifiers are tuned using cross-validation, data from the same types of attacks are available in all folds. This differs from real-world employment where unknown types of attacks may be present. Consequently, we develop a sequential cross-validation procedure so that not all types of attacks will necessarily be present across all folds, in the hope that this would make the tuning of classifiers more robust. Our results indicate that weighted cost matrices can be used effectively with most statistical classifiers and that sequential cross-validation can have a small, but significant effect for certain types of classifiers.
  • Publication
    Accès libre
    Guest Editors' Introduction: Special Section on Learning, Games, and Security
    (2012)
    Dimitrakakis, Christos 
    ;
    Tom Karygiannis
    ;
    Aikaterini Mitrokotsa
    The articles in this special section are devoted to the topic of learning, computer games and system security.