@conference {838,
	title = {Estimating MPdist with SAX and Machine Learning},
	booktitle = {New Trends in Database and Information Systems (Proceedings of ADBIS 2024, Short Papers, Workshops, Doctoral Consortium and Tutorials)},
	year = {2024},
	publisher = {Springer Cham},
	organization = {Springer Cham},
	abstract = {<p>MPdist is a distance measure which considers two time series to be similar if they share many similar subsequences. However, computing MPdist can be slow, especially for large time series. We propose a technique for the approximate computation of MPdist that uses the SAX representation of the time series to quickly estimate the Nearest Neighbor (NN) distance of each subsequence, and then applies a Machine Learning model to correct the accuracy loss incurred. Our method is orders of magnitude faster than the exact computation of MPdist; at the same time, our best approximation computes the NN of a time series with high accuracy. A thorough evaluation of our technique is provided.</p>
},
	keywords = {Distance metric, Machine Learning, MPdist, Random Forest, SAX, Time series},
	doi = {https://doi.org/10.1007/978-3-031-70421-5_32},
	author = {Mihalis Tsoukalos and Pantelis Chronis and Nikos Platis and Costas Vassilakis},
	editor = {Joe Tekli and Johann Gamper and Richard Chbeir and Yannis Manolopoulos and Salma Sassi and Mirjana Ivanovic and Vargas-Solar, Genoveva and Ester Zumpano}
}
@conference {840,
	title = {Predicting Agricultural Product and Supplies Prices using Artificial Intelligence},
	booktitle = {Proceedings of the 16th International Conference on Knowledge Management and Information Systems},
	year = {2024},
	publisher = {SCITEPRESS Digital Library},
	organization = {SCITEPRESS Digital Library},
	abstract = {<p>This work focuses on the prediction of agricultural product and supply prices using historical data and artificial intelligence methods. Agricultural product and supply prices are important for the economy and growth of agriculture. Using modern data analysis and deep learning methods, a forecasting model was developed to help us predict future price trends. The data used include the sales prices of crop products and the purchase prices of agricultural inputs. The developed forecasting methods exhibit high accuracy for predicting the actual prices of products and supplies, with error margins ranging from 0.29\% to 9.8\%, while they can also predict price rises and falls, with respective success rates ranging from 73.29\% to 84.96\%.</p>
},
	keywords = {Agricultural Products, Data Integration, deep learning, Forecasting Models, Machine Learning, Price Forecasting},
	author = {Ioannis Dionissopoulos and Fotis Assimakopoulos and Dimitris Spiliotopoulos and Dionisis Margaris and Costas Vassilakis}
}
@conference {mathas2018a,
	title = {Evaluation of Apache Spot\textquotesingles machine learning capabilities in an SDN/NFV enabled environment},
	booktitle = {Proceedings of the 13th International Conference on Availability, Reliability and Security - ARES 2018},
	year = {2018},
	publisher = {ACM Press},
	organization = {ACM Press},
	abstract = {Software Defined Networking (SDN) and Network Function Virtualisation (NFV) are transforming modern networks towards a service-oriented architecture. At the same time, the cybersecurity industry is rapidly adopting Machine Learning (ML) algorithms to improve detection and mitigation of complex attacks. Traditional intrusion detection systems perform signature-based detection, based on well-known malicious traffic patterns that signify potential attacks. The main drawback of this method is that attack patterns need to be known in advance and signatures must be preconfigured. Hence, typical systems fail to detect a zero-day attack or an attack with unknown signature. This work considers the use of machine learning for advanced anomaly detection, and specifically deploys the Apache Spot ML framework on an SDN/NFV-enabled testbed running cybersecurity services as Virtual Network Functions (VNFs). VNFs are used to capture traffic for ingestion by the ML algorithm and apply mitigation measures in case of a detected anomaly. Apache Spot utilises Latent Dirichlet Allocation to identify anomalous traffic patterns in Netflow, DNS and proxy data. We evaluate the overall performance of Apache Spot by deploying Denial of Service (Slowloris, BoNeSi) and a Data Exfiltration attack (iodine).},
	keywords = {Apache Spot, Latent Dirichlet Allocation, Machine Learning, Network Function Virtualisation, Penetration Testing, SHIELD Project�, Software Defined Networking},
	doi = {10.1145/3230833.3233278},
	author = {Christos M. Mathas and Olga E. Segou and Georgios Xylouris and Dimitris Christinakis and Michail-Alexandros Kourtis and Costas Vassilakis and Anastasios Kourtis}
}
@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}
}
