Automatic differentiation of non-holonomic fast marching for computing most threatening trajectories under sensors surveillance

We consider a two player game, where a first player has to install a surveillance system within an admissible region. The second player needs to enter the the monitored area, visit a target region, and then leave the area, while minimizing his overall probability of detection. Both players know the target region, and the second player knows the surveillance installation details.Optimal trajectories for the second player are computed using a recently developed variant of the fast marching algorithm, which takes into account curvature constraints modeling the second player vehicle maneuverability. The surveillance system optimization leverages a reverse-mode semi-automatic differentiation procedure, estimating the gradient of the value function related to the sensor location in time N log N

Izbira metode ekstrakcije DNK za spremljanje organizma za biotično zatiranje, Gliocladium catenulatum J1446

Gray mold, caused by the fungus Botrytis cinerea, is one of the most common and serious diseases affecting strawberries. Different fungicides are used to manage this disease but fungi can develop resistance on it. Therefore, much attention is devoted to biological methods of control in recent years. Preparation Prestop® MIX (Verdera Oy, Finland) is available in some European countries. It contains a biocontrol agent (BCA), isolate J1446 of the fungus Gliocladium catenulatum, active against grey mold. In the project Bicopoll, a project of European transnational research cooperation project CORE Organic II, we are monitoring the effectiveness of BCA spreading on strawberry flowers by bees and checking for residues of BSA in bee products (honey, pollen). For this purpose, we developed a new, BCA specific real time PCR, which allows us to detect BCA in different samples and quantify it. In the early stages of development, we focused on the development of DNA extraction methods from product Prestop® MIX

Stochastic Metaheuristics as Sampling Techniques using Swarm Intelligence

Optimization problems appear in many fields, as various as identification problems, supervised learning of neural networks, shortest path problems, etc. Metaheuristics [22] are a family of optimization algorithms, often applied to "hard " combinatorial problems for which no more efficient method is known. They have the advantage of being generi

Proactive Botnet Detection and Defense at Internet scale

Botnets provide the basis for various cyber-threats. However, setting up a complex botnet infrastructure often involves registration of domain names in the domain name system (DNS). Active as well as passive monitoring approaches can be used in the detection of domains that are registered for botnets and other malicious activities. We present a novel architecture for proactive botent detection and defense based on large-scale DNS measurement and smart pattern recognition using machine learning

Towards Adversarial Resilience in Proactive Detection of Botnet Domain Names by using MTD

Artificial Intelligence is often part of state-of-the-art Intrusion Detection Systems. However, attackers use Artificial Intelligence to improve their attacks and circumvent IDS systems. Botnets use artificial intelligence to improve their Domain Name Generation Algorithms. Botnets pose a serious threat to networks that are connected to the Internet and are an enabler for many cyber-criminal activities (e.g., DDoS attacks, banking fraud and cyber-espionage) and cause substantial economic damage. To circumvent detection and prevent takedown actions, bot-masters use DGAs to create, maintain and hide C&C infrastructures. Furthermore, botmasters often release its source code to prevent detection, leading to numerous similar botnets that are created and maintained by different botmasters. As these botnets are based on nearly the same source code basis, they often share similar observable behavior. Current work on detection of DGAs is often based on applying machine learning techniques, as they are capable to generalize and to also detect yet unknown derivatives of a known botnets. However, these machine learning based classifiers can be circumvented by applying adversarial learning techniques. As a consequence, there is a need for resilience against adversarial learning in current Intrusion Detection Systems. In our work, we focus on adversarial learning in DNS based IDSs from the perspective of a network operator. Further, we present our concept to make existing and future machine learning based IDSs more resilient against adversarial learning attacks by applying multi-level Moving Target Defense strategies

Self-Healing Systems: Foundations and Challenges

This document summarizes the results of the Working Group 3 - ``Terminology\u27\u27 - at the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 (organized by A. Andrzejak, K. Geihs, O. Shehory and J. Wilkes). The seminar was held from May 10th 2009 to May 15th 2009 in Schloss Dagstuhl~--~Leibniz Center for Informatics

Die Bedeutung des Wetters für die Sportteilnahme

Der Zweck dieser Studie im Rahmen der Magisterarbeit war, herauszufinden, ob das Wetter einen Einfluss auf die Sportteilnahme im Zusammenhang mit den Sportstadien des Berliner Stadien-Modells (Reinhard Fuchs, 2001) und den sportpsychologischen Parametern aus dem MoVo-Prozessmodell (Göhner & Fuchs, 2007) hat. Zur Untersuchung wurden zusätzlich Wetterdaten von diversen Wetterstationen der Zentralanstalt für Meteorologie und Geodynamik (ZAMG) Wien verwendet, um die subjektiven Antworten den objektiven Wetterdaten gegenüberzustellen. Zunächst informiert der allgemeine Theorieteil über die Theorien und Modelle zur Erklärung der Sportteilnahme, der spezielle Theorieteil über das Berliner Stadien-Modell und das MoVo-Prozessmodell. Als Methode wurde eine quantitative Forschungsmethode mittels Fragebogen gewählt. Dieser Fragebogen besteht erstens aus erprobten Messinstrumenten zur Erfassung der Selbstkonkordanz, der Selbstwirksamkeit, der Konsequenzerfahrungen, der Barrieren und des Barrierenmanagements und zweitens aus einem wetterspezifischen Teil mit Fragen zu einzelnen Wetterfaktoren und Verhalten bei Schlechtwetter. Die Ergebnisse (mit N = 397 Teilnehmer) zeigen, dass die objektiven Wetterdaten keinen relevanten Zusammenhang mit dem Verhalten bei Schlechtwetter zeigen. Die Stadien unterscheiden sich hinsichtlich des Verhaltens bei Schlechtwetter zum Teil. Speziell die Personen des Stadiums Habituation (n = 265) treiben am ehesten auch bei Schlechtwetter ihre Hauptsportart, die Personen des Stadiums Implementation (n = 41) am wenigsten. Die Personen im Stadium Fluktuation (n = 57) haben die wenigsten Gegenmaßnahmen (z.B. alternative Sportaktivität) bei Schlechtwetter, jene im Stadium der Implementation die meisten. Die Stichproben der Stadien Präkontemplation (n = 6), Kontemplation (n = 7), Disposition (n = 6) und Präaktion (n = 14) zeigen sich bei den Maßnahmen bei Schlechtwetter nicht repräsentativ. Resumption (n = 1) wurde aus allen Berechnungen herausgenommen. Das Schlechtwetterverhalten spiegelt sich zusätzlich in den sportpsychologischen Parametern wider. Höhere Selbstkonkordanz, Selbstwirksamkeit und positive Konsequenzerfahrungen erweisen sich besonders als förderlich. Zusammengefasst ist das Verhalten bei Schlechtwetter in den Stadien zum Teil unterschiedlich, jedoch sind weitere Studien dazu nötig, die das Wetter als generell ernstzunehmende Barriere hervorheben. Dies würde dann eine intensivere Thematisierung des Wetters bei Interventionsprogrammen befürworten