@conference {DBLP:conf/debs/ChantziosZST19,
	title = {Ping - A customizable, open-source information filtering system for textual data},
	booktitle = {Proceedings of the 13th ACM International Conference on Distributed and Event-based Systems, DEBS 2019, Darmstadt, Germany, June 24-28, 2019.},
	year = {2019},
	pages = {228{\textendash}231},
	doi = {10.1145/3328905.3332512},
	url = {https://doi.org/10.1145/3328905.3332512},
	author = {Thanasis Chantzios and Lefteris Zervakis and Spiros Skiadopoulos and Christos Tryfonopoulos}
}
@conference {588,
	title = {The quest for the appropriate cyber-threat intelligence sharing platform},
	booktitle = {Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA), Prague, Czech Republic},
	year = {2019},
	month = { 07/2009},
	abstract = {Cyber-threat intelligence (CTI) is any information that can help an organization identify, assess, monitor, and respond to cyber-threats. It relates to all cyber components of an organization such as networks, computers, and other types of information technology. In the recent years, due to the major increase of cyber-threats, CTI sharing is becoming increasingly important both as a subject of research and as a concept of providing additional security to organizations. However, selecting the proper tools and platforms for CTI sharing, is a challenging task, that pertains to a variety of aspects. In this paper, we start by overviewing the CTI procedure (threat types, categories, sources and the general CTI life-cycle). Then, we present a set of seven high-level CTI platform recommendations that can be used to evaluate a platform and subsequently we survey six state-of- the-art cyber-threat intelligence platforms. Finally, we compare and evaluate the six aforementioned platforms by means of the earlier proposed recommendations.},
	keywords = {Cyber-threat, Intelligence, Sharing},
	url = {http://users.uop.gr/~trifon/papers/pdf/data19-CKSKTBK.pdf},
	author = {Thanasis Chantzios and Paris Koloveas and Spiros Skiadopoulos and Nikos Kolokotronis and Christos Tryfonopoulos and Vasiliki-Georgia Bilali and Dimitris Kavallieros}
}
@conference {591,
	title = {A crawler architecture for harvesting the clear, social, and dark web for IoT-related cyber-threat intelligence},
	booktitle = {In Proceedings of the IEEE Workshop on Cyber Security \& Resilience in the Internet of Things (CSRIoT @ IEEE Services)},
	year = {2016},
	month = {July 2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Milan, Italy},
	abstract = {The clear, social, and dark web have lately been identified as rich sources of valuable cyber-security information that {\textendash}given the appropriate tools and methods{\textendash} may be identified, crawled and subsequently leveraged to actionable cyber- threat intelligence. In this work, we focus on the information gathering task, and present a novel crawling architecture for transparently harvesting data from security websites in the clear web, security forums in the social web, and hacker fo- rums/marketplaces in the dark web. The proposed architecture adopts a two-phase approach to data harvesting. Initially a machine learning-based crawler is used to direct the harvesting towards websites of interest, while in the second phase state- of-the-art statistical language modelling techniques are used to represent the harvested information in a latent low-dimensional feature space and rank it based on its potential relevance to the task at hand. The proposed architecture is realised using exclusively open-source tools, and a preliminary evaluation with crowdsourced results demonstrates its effectiveness.},
	keywords = {crawling architecture, cyber-security, cyber-threat intelligence, IoT, language models, Machine Learning},
	url = {http://users.uop.gr/ trifon/papers/pdf/csriot19-KCTS.pdf},
	author = {Paris Koloveas and Thanasis Chantzios and Christos Tryfonopoulos and Spiros Skiadopoulos}
}
