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See detailKnowledge Discovery Approach from Blockchain, Crypto-currencies, and Financial Stock Exchanges
Lagraa, Sofiane UL; Charlier, Jérémy Henri J. UL; State, Radu UL

Poster (2018, August 20)

Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one ... [more ▼]

Last few years have witnessed a steady growth in interest on crypto-currencies and blockchains. They are receiving considerable interest from industry and the research community, the most popular one being Bitcoin. However, these crypto-currencies are so far relatively poorly analyzed and investigated. Recently, many solutions, mostly based on ad-hoc engineered solutions, are being developed to discover relevant analysis from crypto-currencies, but are not sufficient to understand behind crypto-currencies. In this paper, we provide a deep analysis of crypto-currencies by proposing a new knowledge discovery approach for each crypto-currency, across crypto-currencies, blockchains, and financial stocks. The novel approach is based on a conjoint use of data mining algorithms on imbalanced time series. It automatically reports co-variation dependency patterns of the time series. The experiments on the public crypto-currencies and financial stocks markets data also demonstrate the usefulness of the approach by discovering the different relationships across multiple time series sources and insights correlations behind crypto-currencies. [less ▲]

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See detailDetecting Malicious Authentication Events Trustfully
Kaiafas, Georgios UL; Varisteas, Georgios UL; Lagraa, Sofiane UL et al

in Kaiafas, Georgios; Varisteas, Georgios; Lagraa, Sofiane (Eds.) et al IEEE/IFIP Network Operations and Management Symposium, 23-27 April 2018, Taipei, Taiwan Cognitive Management in a Cyber World (2018)

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See detailProfiling Smart Contracts Interactions Tensor Decomposition and Graph Mining.
Charlier, Jérémy Henri J. UL; Lagraa, Sofiane UL; State, Radu UL et al

in Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017) co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18, 2017. (2017, September)

Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’s crypto-currency. They provide higher transaction security and ... [more ▼]

Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bitcoin’s crypto-currency. They provide higher transaction security and allow economy of scale through the automated process. Smart contracts provides inherent benefits for financial institutions such as investment banking, retail banking, and insurance. This technology is widely used within Ethereum, an open source block-chain platform, from which the data has been extracted to conduct the experiments. In this work, we propose an multi-dimensional approach to find and predict smart contracts interactions only based on their crypto-currency exchanges. This approach relies on tensor modeling combined with stochastic processes. It underlines actual exchanges between smart contracts and targets the predictions of future interactions among the community. The tensor analysis is also challenged with the latest graph algorithms to assess its strengths and weaknesses in comparison to a more standard approach. [less ▲]

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See detailBotGM: Unsupervised Graph Mining to Detect Botnets in Traffic Flows
Lagraa, Sofiane UL; François, Jérôme; Lahmadi, Abdelkader et al

in CSNet 2017 Conference Proceedings (2017)

Botnets are one of the most dangerous and serious cybersecurity threats since they are a major vector of large-scale attack campaigns such as phishing, distributed denial-of-service (DDoS) attacks ... [more ▼]

Botnets are one of the most dangerous and serious cybersecurity threats since they are a major vector of large-scale attack campaigns such as phishing, distributed denial-of-service (DDoS) attacks, trojans, spams, etc. A large body of research has been accomplished on botnet detection, but recent security incidents show that there are still several challenges remaining to be addressed, such as the ability to develop detectors which can cope with new types of botnets. In this paper, we propose BotGM, a new approach to detect botnet activities based on behavioral analysis of network traffic flow. BotGM identifies network traffic behavior using graph-based mining techniques to detect botnets behaviors and model the dependencies among flows to traceback the root causes then. We applied BotGM on a publicly available large dataset of Botnet network flows, where it detects various botnet behaviors with a high accuracy without any prior knowledge of them. [less ▲]

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