Per-host DDoS mitigation by direct-control reinforcement learning

DDoS attacks plague the availability of online services today, yet like many cybersecurity problems are evolving and non-stationary. Normal and attack patterns shift as new protocols and applications are introduced, further compounded by burstiness and seasonal variation. Accordingly, it is difficult to apply machine learning-based techniques and defences in practice. Reinforcement learning (RL) may overcome this detection problem for DDoS attacks by managing and monitoring consequences; an agent’s role is to learn to optimise performance criteria (which are always available) in an online manner. We advance the state-of-the-art in RL-based DDoS mitigation by introducing two agent classes designed to act on a per-flow basis, in a protocol-agnostic manner for any network topology. This is supported by an in-depth investigation of feature suitability and empirical evaluation. Our results show the existence of flow features with high predictive power for different traffic classes, when used as a basis for feedback-loop-like control. We show that the new RL agent models can offer a significant increase in goodput of legitimate TCP traffic for many choices of host density

Processing social media text for the quantamental analyses of cryptoasset time series

This thesis analyses social media text to identify which events and concerns are associated with changes between phases of rising and falling cryptoasset prices. A new cryptoasset classification system, based on token functionality, highlights Bitcoin as the largest example of a 'crypto-transaction' system and Ethereum as the largest example of a 'crypto-fuel' system. The price of ether is only weakly correlated with that of bitcoin (Spearman's rho 0.3849). Both bitcoin and ether show distinct phases of rising or falling prices and have a large, dedicated social media forum on Reddit. A process is developed to extract events and concerns discussed on social media associated with these different phases of price movement. This innovative data-driven approach circumvents the need to pre-judge social media metrics. First, a new, non-parametric Data-Driven Phasic Word Identification methodology is developed to find words associated with the phase of declining bitcoin prices in 2017-18. This approach is further developed to find the context of these words, from which topics are inferred. Then, neural networks (word2vec) are applied to evolve analysis from extracting words to extracting topics. Finally, this work enables the development of a framework for identifying which events and concerns are plausible causes of changes between different phases in the ether and bitcoin price series. Consistent with Bitcoin providing a form of money and Ethereum providing a platform for developing applications, these results show the one-off effect of regulatory bans on bitcoin, and the recurring effects of rival innovations on ether price. The results also suggest the influence of technical traders, captured through market price discourse, on both cryptoassets. This thesis demonstrates the value of a quantamental approach to the analysis of cryptoasset prices

Search beyond traditional probabilistic information retrieval

"This thesis focuses on search beyond probabilistic information retrieval. Three ap- proached are proposed beyond the traditional probabilistic modelling. First, term associ- ation is deeply examined. Term association considers the term dependency using a factor analysis based model, instead of treating each term independently. Latent factors, con- sidered the same as the hidden variables of ""eliteness"" introduced by Robertson et al. to gain understanding of the relation among term occurrences and relevance, are measured by the dependencies and occurrences of term sequences and subsequences. Second, an entity-based ranking approach is proposed in an entity system named ""EntityCube"" which has been released by Microsoft for public use. A summarization page is given to summarize the entity information over multiple documents such that the truly relevant entities can be highly possibly searched from multiple documents through integrating the local relevance contributed by proximity and the global enhancer by topic model. Third, multi-source fusion sets up a meta-search engine to combine the ""knowledge"" from different sources. Meta-features, distilled as high-level categories, are deployed to diversify the baselines. Three modified fusion methods are employed, which are re- ciprocal, CombMNZ and CombSUM with three expanded versions. Through extensive experiments on the standard large-scale TREC Genomics data sets, the TREC HARD data sets and the Microsoft EntityCube Web collections, the proposed extended models beyond probabilistic information retrieval show their effectiveness and superiority.

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band I

Übersicht der Teilkonferenzen Band I: • 11. Konferenz Mobilität und Digitalisierung (MMS 2016) • Automated Process und Service Management • Business Intelligence, Analytics und Big Data • Computational Mobility, Transportation and Logistics • CSCW & Social Computing • Cyber-Physische Systeme und digitale Wertschöpfungsnetzwerke • Digitalisierung und Privacy • e-Commerce und e-Business • E-Government – Informations- und Kommunikationstechnologien im öffentlichen Sektor • E-Learning und Lern-Service-Engineering – Entwicklung, Einsatz und Evaluation technikgestützter Lehr-/Lernprozess

Analysemethoden, Anwendungsfälle und Werkzeuge des Social CRM

Der Forschungsbericht enthält studentische Arbeiten zu Analysemethoden, Anwendungsfällen und Technologien des Social Customer Relationship Management (CRM). Die einzelnen Beiträge betrachten das Konzept eines integrierten Social CRM aus verschiedenen Perspektiven und anhand konkreter Beispiele. Grundlage des Forschungsberichts sind Ergebnisse aus zurückliegenden Forschungsprojekten des Instituts für Wirtschaftsinformatik und des Seminars Enterprise Systems 2 an der Universität Leipzig.:Inhaltsverzeichnis Abbildungsverzeichnis V Tabellenverzeichnis VII Abkürzungsverzeichnis VIII Vorwort XI Konzeptionelle Grundlagen Olaf Reinhold Von der Social Media-Nutzung zum Integrierten Social CRM: The-matische Einführung und Strukturierung des Arbeitsheftes 3 Analysemethoden Hans-Georg Wu Text Mining im Social CRM 15 Franziska Suchy Analyseansätze im Social CRM 29 Martin Lebik Methoden zur Ermittlung von Influencern 41 Anwendungsfälle Eva Kahlert Einsatz und Nutzen von Social Media in einem KMU am Beispiel des Outdoor-Unternehmens tapir 63 Veronika Prochotská Szenarios zum Präsenzaufbau im Social CRM 83 Ana Maria Cerlinca Social Media Monitoring und Dashboards zur Unterstützung universitärer Prozesse 97 Richard Stüber Die Social Media-Nutzung einer deutschen und einer brasiliani-schen Universität im Vergleich 111 Werkzeuge Marcel Fischer Prozessunterstützung durch SCRM-Werkzeuge 125 Tom Roick Systeme zur Ermittlung von Influencern 135 Jonas Buch SCRM-Unterstützungssysteme zum Präsenzaufbau im Social Web 147 Datenmanagement im Social CRM Mattis Hartwig Data Aggregation in Social CRM 165 Karsten Stöcker Vergleichende Betrachtung der Application Programming Interfaces sozialer Netzwerke 175 Anhang - Poster 18

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI