@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}
}
@article {581,
	title = {Query Reorganisation Algorithms for Efficient Boolean Information Filtering},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	volume = {29},
	year = {2017},
	pages = {418{\textendash}432},
	abstract = {In the information filtering paradigm, clients subscribe to a server with continuous queries that express their information needs and get notified every time appropriate information is published. To perform this task in an efficient way, servers employ indexing schemes that support fast matches of the incoming information with the query database. Such indexing schemes involve (i) main-memory trie-based data structures that cluster similar queries by capturing common elements between them and (ii) efficient filtering mechanisms that exploit this clustering to achieve high throughput and low filtering times. However, state-of-the-art indexing schemes are sensitive to the query insertion order and cannot adopt to an evolving query workload, degrading the filtering performance over time. In this paper, we present an adaptive trie-based algorithm that outperforms current methods by relying on query statistics to reorganise the query database. Contrary to previous approaches, we show that the nature of the constructed tries, rather than their compactness, is the determining factor for efficient filtering performance. Our algorithm does not depend on the order of insertion of queries in the database, manages to cluster queries even when clustering possibilities are limited, and achieves more than 96\% filtering time improvement over its state-of-the-art competitors. Finally, we demonstrate that our solution is easily extensible to multi-core machines.},
	keywords = {dissemination, indexing methods, Information filtering, user profiles and alert services},
	doi = {10.1109/TKDE.2016.2620140},
	url = {https://doi.org/10.1109/TKDE.2016.2620140},
	author = {Lefteris Zervakis and Christos Tryfonopoulos and Spiros Skiadopoulos and Manolis Koubarakis}
}
@conference {567,
	title = {Query Processing in Super-Peer Networks with Languages Based on Information Retrieval: the P2P-DIET Approach},
	booktitle = {Proceedings of the International Workshop on Peer-to-peer Computing and Databases (P2P\&DB @ EDBT)},
	year = {2004},
	month = {March},
	address = {Herakion, Greece},
	author = {Stratos Idreos and Christos Tryfonopoulos and Manolis Koubarakis and Yannis Drougas}
}
