31 research outputs found

    Data Analytics as a Service: A look inside the PANACEA project

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    Understanding and Leveraging Virtualization Technology in Commodity Computing Systems

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    Commodity computing platforms are imperfect, requiring various enhancements for performance and security purposes. In the past decade, virtualization technology has emerged as a promising trend for commodity computing platforms, ushering many opportunities to optimize the allocation of hardware resources. However, many abstractions offered by virtualization not only make enhancements more challenging, but also complicate the proper understanding of virtualized systems. The current understanding and analysis of these abstractions are far from being satisfactory. This dissertation aims to tackle this problem from a holistic view, by systematically studying the system behaviors. The focus of our work lies in performance implication and security vulnerabilities of a virtualized system.;We start with the first abstraction---an intensive memory multiplexing for I/O of Virtual Machines (VMs)---and present a new technique, called Batmem, to effectively reduce the memory multiplexing overhead of VMs and emulated devices by optimizing the operations of the conventional emulated Memory Mapped I/O in hypervisors. Then we analyze another particular abstraction---a nested file system---and attempt to both quantify and understand the crucial aspects of performance in a variety of settings. Our investigation demonstrates that the choice of a file system at both the guest and hypervisor levels has significant impact upon I/O performance.;Finally, leveraging utilities to manage VM disk images, we present a new patch management framework, called Shadow Patching, to achieve effective software updates. This framework allows system administrators to still take the offline patching approach but retain most of the benefits of live patching by using commonly available virtualization techniques. to demonstrate the effectiveness of the approach, we conduct a series of experiments applying a wide variety of software patches. Our results show that our framework incurs only small overhead in running systems, but can significantly reduce maintenance window

    Federation of Cyber Ranges

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    Küberkaitse võimekuse aluselemendiks on kõrgete oskustega ja kokku treeninud spetsialistid. Tehnikute, operaatorite ja otsustajate teadlikkust ja oskusi saab treenida läbi rahvusvaheliste õppuste. On mõeldamatu, et kaitse ja rünnakute harjutamiseks kasutatakse toimivat reaalajalist organisatsiooni IT-süsteemi. Päriseluliste süsteemide simuleerimiseks on võimalik kasutada küberharjutusväljakuid.NATO ja Euroopa Liidu liikmesriikides on mitmed juba toimivad ja käimasolevad arendusprojektid uute küberharjutusväljakute loomiseks. Et olemasolevast ressurssi täies mahus kasutada, tuleks kõik sellised harjutusväljakud rahvusvaheliste õppuste tarbeks ühendada. Ühenduvus on võimalik saavutada alles pärast kokkuleppeid, tehnoloogiate ja erinevate harjutusväljakute kitsenduste arvestamist.Antud lõputöö vaatleb kahte küberharjutusväljakut ja uurib võimalusi, kuidas on võimalik rahvuslike harjutusväljakute ressursse jagada ja luua ühendatud testide ja õppuste keskkond rahvusvahelisteks küberkaitseõppusteks. Lõputöö annab soovitusi informatsiooni voogudest, testkontseptsioonidest ja eeldustest, kuidas saavutada ühendused ressursside jagamise võimekusega. Vaadeldakse erinevaid tehnoloogiad ja operatsioonilisi aspekte ning hinnatakse nende mõju.Et paremini mõista harjutusväljakute ühendamist, on üles seatud testkeskkond Eesti ja Tšehhi laborite infrastruktuuride vahel. Testiti erinevaid võrguparameetreid, operatsioone virtuaalmasinatega, virtualiseerimise tehnoloogiad ning keskkonna haldust avatud lähtekoodiga tööriistadega. Testide tulemused olid üllatavad ja positiivsed, muutes ühendatud küberharjutusväljakute kontseptsiooni saavutamise oodatust lihtsamaks.Magistritöö on kirjutatud inglise keeles ja sisaldab teksti 42 leheküljel, 7 peatükki, 12 joonist ja 4 tabelit.Võtmesõnad:Küberharjutusväljak, NATO, ühendamine, virtualiseerimine, rahvusvahelised küberkaitse õppusedAn essential element of the cyber defence capability is highly skilled and well-trained personnel. Enhancing awareness and education of technicians, operators and decision makers can be done through multinational exercises. It is unthinkable to use an operational production environment to train attack and defence of the IT system. For simulating a life like environment, a cyber range can be used. There are many emerging and operational cyber ranges in the EU and NATO. To benefit more from available resources, a federated cyber range environment for multinational cyber defence exercises can be built upon the current facilities. Federation can be achieved after agreements between nations and understanding of the technologies and limitations of different national ranges.This study compares two cyber ranges and looks into possibilities of pooling and sharing of national facilities and to the establishment of a logical federation of interconnected cyber ranges. The thesis gives recommendations on information flow, proof of concept, guide-lines and prerequisites to achieve an initial interconnection with pooling and sharing capabilities. Different technologies and operational aspects are discussed and their impact is analysed. To better understand concepts and assumptions of federation, a test environment with Estonian and Czech national cyber ranges was created. Different aspects of network parameters, virtual machine manipulations, virtualization technologies and open source administration tools were tested. Some surprising and positive outcomes were in the result of the tests, making logical federation technologically easier and more achievable than expected.The thesis is in English and contains 42 pages of text, 7 chapters, 12 figures and 4 tables.Keywords:Cyber Range, NATO, federation, virtualization, multinational cyber defence exercise

    Fault diagnosis for IP-based network with real-time conditions

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    BACKGROUND: Fault diagnosis techniques have been based on many paradigms, which derive from diverse areas and have different purposes: obtaining a representation model of the network for fault localization, selecting optimal probe sets for monitoring network devices, reducing fault detection time, and detecting faulty components in the network. Although there are several solutions for diagnosing network faults, there are still challenges to be faced: a fault diagnosis solution needs to always be available and able enough to process data timely, because stale results inhibit the quality and speed of informed decision-making. Also, there is no non-invasive technique to continuously diagnose the network symptoms without leaving the system vulnerable to any failures, nor a resilient technique to the network's dynamic changes, which can cause new failures with different symptoms. AIMS: This thesis aims to propose a model for the continuous and timely diagnosis of IP-based networks faults, independent of the network structure, and based on data analytics techniques. METHOD(S): This research's point of departure was the hypothesis of a fault propagation phenomenon that allows the observation of failure symptoms at a higher network level than the fault origin. Thus, for the model's construction, monitoring data was collected from an extensive campus network in which impact link failures were induced at different instants of time and with different duration. These data correspond to widely used parameters in the actual management of a network. The collected data allowed us to understand the faults' behavior and how they are manifested at a peripheral level. Based on this understanding and a data analytics process, the first three modules of our model, named PALADIN, were proposed (Identify, Collection and Structuring), which define the data collection peripherally and the necessary data pre-processing to obtain the description of the network's state at a given moment. These modules give the model the ability to structure the data considering the delays of the multiple responses that the network delivers to a single monitoring probe and the multiple network interfaces that a peripheral device may have. Thus, a structured data stream is obtained, and it is ready to be analyzed. For this analysis, it was necessary to implement an incremental learning framework that respects networks' dynamic nature. It comprises three elements, an incremental learning algorithm, a data rebalancing strategy, and a concept drift detector. This framework is the fourth module of the PALADIN model named Diagnosis. In order to evaluate the PALADIN model, the Diagnosis module was implemented with 25 different incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming scenario) as the rebalancing strategy. On the other hand, a dataset was built through the first modules of the PALADIN model (SOFI dataset), which means that these data are the incoming data stream of the Diagnosis module used to evaluate its performance. The PALADIN Diagnosis module performs an online classification of network failures, so it is a learning model that must be evaluated in a stream context. Prequential evaluation is the most used method to perform this task, so we adopt this process to evaluate the model's performance over time through several stream evaluation metrics. RESULTS: This research first evidences the phenomenon of impact fault propagation, making it possible to detect fault symptoms at a monitored network's peripheral level. It translates into non-invasive monitoring of the network. Second, the PALADIN model is the major contribution in the fault detection context because it covers two aspects. An online learning model to continuously process the network symptoms and detect internal failures. Moreover, the concept-drift detection and rebalance data stream components which make resilience to dynamic network changes possible. Third, it is well known that the amount of available real-world datasets for imbalanced stream classification context is still too small. That number is further reduced for the networking context. The SOFI dataset obtained with the first modules of the PALADIN model contributes to that number and encourages works related to unbalanced data streams and those related to network fault diagnosis. CONCLUSIONS: The proposed model contains the necessary elements for the continuous and timely diagnosis of IPbased network faults; it introduces the idea of periodical monitorization of peripheral network elements and uses data analytics techniques to process it. Based on the analysis, processing, and classification of peripherally collected data, it can be concluded that PALADIN achieves the objective. The results indicate that the peripheral monitorization allows diagnosing faults in the internal network; besides, the diagnosis process needs an incremental learning process, conceptdrift detection elements, and rebalancing strategy. The results of the experiments showed that PALADIN makes it possible to learn from the network manifestations and diagnose internal network failures. The latter was verified with 25 different incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming scenario) as the rebalancing strategy. This research clearly illustrates that it is unnecessary to monitor all the internal network elements to detect a network's failures; instead, it is enough to choose the peripheral elements to be monitored. Furthermore, with proper processing of the collected status and traffic descriptors, it is possible to learn from the arriving data using incremental learning in cooperation with data rebalancing and concept drift approaches. This proposal continuously diagnoses the network symptoms without leaving the system vulnerable to failures while being resilient to the network's dynamic changes.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Juan Carlos Dueñas López.- Vocal: Juan Manuel Corchado Rodrígue

    Data Flooding against Ransomware: Concepts and Implementations

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    Ransomware is one of the most infamous kinds of malware, particularly the “crypto” subclass, which encrypts users’ files, asking for some monetary ransom in exchange for the decryption key. Recently, crypto-ransomware grew into a scourge for enterprises and governmental institutions. The most recent and impactful cases include an oil company in the US, an international Danish shipping company, and many hospitals and health departments in Europe. Attacks result in production lockdowns, shipping delays, and even risks to human lives. To contrast ransomware attacks (crypto, in particular), we propose a family of solutions, called Data Flooding against Ransomware, tackling the main phases of detection, mitigation, and restoration, based on a mix of honeypots, resource contention, and moving target defence. These solutions hinge on detecting and contrasting the action of ransomware by flooding specific locations (e.g., the attack location, sensible folders, etc.) of the victim’s disk with files. Besides the abstract definition of this family of solutions, we present an open-source tool that implements the mitigation and restoration phases, called Ranflood. In particular, Ranflood supports three flooding strategies, apt for different attack scenarios. At its core, Ranflood buys time for the user to counteract the attack, e.g., to access an unresponsive, attacked server and shut it down manually. We benchmark the efficacy of Ranflood by performing a thorough evaluation over 6 crypto-ransomware (e.g., WannaCry, LockBit) for a total of 78 different attack scenarios, showing that Ranflood consistently lowers the amount of files lost to encryption
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