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  • 1.
    Argyriou, Marios
    et al.
    DTU Compute, Technical University of Denmark, Lyngby, Denmark.
    Dragoni, Nicola
    Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Lyngby, Denmark.
    Spognardi, Angelo
    Dipartimento Informatica, Sapienza Università di Roma, Rome, Italy.
    Analysis and Evaluation of SafeDroid v2.0, a Framework for Detecting Malicious Android Applications2018In: Security and Communication Networks, ISSN 1939-0114, E-ISSN 1939-0122, article id UNSP 4672072Article in journal (Refereed)
    Abstract [en]

    Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead.

  • 2.
    De Donno, Michele
    et al.
    DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark.
    Dragoni, Nicola
    Örebro University, School of Science and Technology. DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark.
    Giaretta, Alberto
    Örebro University, School of Science and Technology.
    Spognardi, Angelo
    Computer Science Department, Sapienza University of Rome, Rome, Italy.
    DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation2018In: Security and Communication Networks, ISSN 1939-0114, E-ISSN 1939-0122, article id 7178164Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far.

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