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Dr. Christos Tryfonopoulos
Software and Database Systems (SoDa) Lab, Department of Informatics & Telecommunications, University of the Peloponnese, University Campus - Akadimaikou G.K. Vlachou Str., GR22131 Tripoli, Greece

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0 Big Data
0 Data Science
0 Distributed Systems
0 Information Retrieval
0 Data & user anonymity

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Journal article
Published: 30 March 2021 in Electronics
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In today’s world, technology has become deep-rooted and more accessible than ever over a plethora of different devices and platforms, ranging from company servers and commodity PCs to mobile phones and wearables, interconnecting a wide range of stakeholders such as households, organizations and critical infrastructures. The sheer volume and variety of the different operating systems, the device particularities, the various usage domains and the accessibility-ready nature of the platforms creates a vast and complex threat landscape that is difficult to contain. Staying on top of these evolving cyber-threats has become an increasingly difficult task that presently relies heavily on collecting and utilising cyber-threat intelligence before an attack (or at least shortly after, to minimize the damage) and entails the collection, analysis, leveraging and sharing of huge volumes of data. In this work, we put forward inTIME, a machine learning-based integrated framework that provides an holistic view in the cyber-threat intelligence process and allows security analysts to easily identify, collect, analyse, extract, integrate, and share cyber-threat intelligence from a wide variety of online sources including clear/deep/dark web sites, forums and marketplaces, popular social networks, trusted structured sources (e.g., known security databases), or other datastore types (e.g., pastebins). inTIME is a zero-administration, open-source, integrated framework that enables security analysts and security stakeholders to (i) easily deploy a wide variety of data acquisition services (such as focused web crawlers, site scrapers, domain downloaders, social media monitors), (ii) automatically rank the collected content according to its potential to contain useful intelligence, (iii) identify and extract cyber-threat intelligence and security artifacts via automated natural language understanding processes, (iv) leverage the identified intelligence to actionable items by semi-automatic entity disambiguation, linkage and correlation, and (v) manage, share or collaborate on the stored intelligence via open standards and intuitive tools. To the best of our knowledge, this is the first solution in the literature to provide an end-to-end cyber-threat intelligence management platform that is able to support the complete threat lifecycle via an integrated, simple-to-use, yet extensible framework.

ACS Style

Paris Koloveas; Thanasis Chantzios; Sofia Alevizopoulou; Spiros Skiadopoulos; Christos Tryfonopoulos. inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence. Electronics 2021, 10, 818 .

AMA Style

Paris Koloveas, Thanasis Chantzios, Sofia Alevizopoulou, Spiros Skiadopoulos, Christos Tryfonopoulos. inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence. Electronics. 2021; 10 (7):818.

Chicago/Turabian Style

Paris Koloveas; Thanasis Chantzios; Sofia Alevizopoulou; Spiros Skiadopoulos; Christos Tryfonopoulos. 2021. "inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence." Electronics 10, no. 7: 818.

Journal article
Published: 23 April 2020 in Big Data and Cognitive Computing
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Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of Things (IoT) devices, have allowed us to enrich and customize the cultural experience, but at the same time have created an avalanche of new data that needs to be stored and appropriately managed in order to be of value. Although data management plays a central role in driving forward the cultural heritage domain, the solutions applied so far are fragmented, physically distributed, require specialized IT knowledge to deploy, and entail significant IT experience to operate even for trivial tasks. In this work, we present Hydria, an online data lake that allows users without any IT background to harvest, store, organize, analyze and share heterogeneous, multi-faceted cultural heritage data. Hydria provides a zero-administration, zero-cost, integrated framework that enables researchers, museum curators and other stakeholders within the cultural heritage domain to easily (i) deploy data acquisition services (like social media scrapers, focused web crawlers, dataset imports, questionnaire forms), (ii) design and manage versatile customizable data stores, (iii) share whole datasets or horizontal/vertical data shards with other stakeholders, (iv) search, filter and analyze data via an expressive yet simple-to-use graphical query engine and visualization tools, and (v) perform user management and access control operations on the stored data. To the best of our knowledge, this is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background.

ACS Style

Kimon Deligiannis; Paraskevi Raftopoulou; Christos Tryfonopoulos; Nikos Platis; Costas Vassilakis. Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain. Big Data and Cognitive Computing 2020, 4, 7 .

AMA Style

Kimon Deligiannis, Paraskevi Raftopoulou, Christos Tryfonopoulos, Nikos Platis, Costas Vassilakis. Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain. Big Data and Cognitive Computing. 2020; 4 (2):7.

Chicago/Turabian Style

Kimon Deligiannis; Paraskevi Raftopoulou; Christos Tryfonopoulos; Nikos Platis; Costas Vassilakis. 2020. "Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain." Big Data and Cognitive Computing 4, no. 2: 7.

Conference paper
Published: 14 May 2016 in Transactions on Petri Nets and Other Models of Concurrency XV
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In this work, we envision a publish/subscribe ontology system that is able to index large numbers of expressive continuous queries and filter them against RDF data that arrive in a streaming fashion. To this end, we propose a SPARQL extension that supports the creation of full-text continuous queries and propose a family of main-memory query indexing algorithms which perform matching at low complexity and minimal filtering time. We experimentally compare our approach against a state-of-the-art competitor (extended to handle indexing of full-text queries) both on structural and full-text tasks using real-world data. Our approach proves two orders of magnitude faster than the competitor in all types of filtering tasks.

ACS Style

Lefteris Zervakis; Christos Tryfonopoulos; Spiros Skiadopoulos; Manolis Koubarakis. Full-Text Support for Publish/Subscribe Ontology Systems. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 9678, 233 -249.

AMA Style

Lefteris Zervakis, Christos Tryfonopoulos, Spiros Skiadopoulos, Manolis Koubarakis. Full-Text Support for Publish/Subscribe Ontology Systems. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; 9678 ():233-249.

Chicago/Turabian Style

Lefteris Zervakis; Christos Tryfonopoulos; Spiros Skiadopoulos; Manolis Koubarakis. 2016. "Full-Text Support for Publish/Subscribe Ontology Systems." Transactions on Petri Nets and Other Models of Concurrency XV 9678, no. : 233-249.

Book chapter
Published: 01 January 2015 in Medical Data Privacy Handbook
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Publishing medical datasets about individuals, in a privacy-preserving way, has led to a significant body of research. Meanwhile, algorithms for anonymizing such datasets, with relational or set-valued (a.k.a. transaction) attributes, in a way that preserves data truthfulness, are crucial to medical research. Selecting, however, the most appropriate algorithm is still far from trivial, and tools that assist data publishers in this task are needed. To highlight this need, we initially provide a brief description of the popular anonymization algorithms and the most commonly used metrics to quantify data utility. Next, we present a system that we have designed, which is capable of applying a set of anonymization algorithms, enabling data holders, including medical researchers and healthcare organizations, to test the effectiveness and efficiency of different methods. Our system, called SECRETA, allows evaluating a specific anonymization algorithm, comparing multiple anonymization algorithms, and combining algorithms for anonymizing datasets with both relational and transaction attributes. The analysis of the algorithms is performed in an interactive and progressive way, and results, including attribute statistics and various data utility indicators, are summarized and presented graphically.

ACS Style

Giorgos Poulis; Aris Gkoulalas-Divanis; Grigorios Loukides; Spiros Skiadopoulos; Christos Tryfonopoulos. SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets. Medical Data Privacy Handbook 2015, 83 -109.

AMA Style

Giorgos Poulis, Aris Gkoulalas-Divanis, Grigorios Loukides, Spiros Skiadopoulos, Christos Tryfonopoulos. SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets. Medical Data Privacy Handbook. 2015; ():83-109.

Chicago/Turabian Style

Giorgos Poulis; Aris Gkoulalas-Divanis; Grigorios Loukides; Spiros Skiadopoulos; Christos Tryfonopoulos. 2015. "SECRETA: A Tool for Anonymizing Relational, Transaction and RT-Datasets." Medical Data Privacy Handbook , no. : 83-109.