Hypergraph Partitioning With Embeddings

Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein "hyperedges" may connect any number of nodes. As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate. State-of-the-art algorithms that solve this problem follow the multilevel paradigm, which begins by iteratively "coarsening" the input hypergraph to smaller problem instances that share key structural features. Once identifying an approximate problem that is small enough to be solved directly, that solution can be interpolated and refined to the original problem. While this strategy represents an excellent trade off between quality and running time, it is sensitive to coarsening strategy. In this work we propose using graph embeddings of the initial hypergraph in order to ensure that coarsened problem instances retrain key structural features. Our approach prioritizes coarsening within self-similar regions within the input graph, and leads to significantly improved solution quality across a range of considered hypergraphs. Reproducibility: All source code, plots and experimental data are available at https://sybrandt.com/2019/partition

Umělá inteligence v kybernetické bezpečnosti

Artifcial intelligence (AI) and machine learning (ML) have grown rapidly in recent years, and their applications in practice can be seen in many felds, ranging from facial recognition to image analysis. Recent developments in Artificial intelligence have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and likely to adapt AI techniques to their operations. This thesis presents advances made so far in the field of applying AI techniques in cybersecurity for combating against cyber threats, to demonstrate how this promising technology can be a useful tool for detection and prevention of cyberattacks. Furthermore, the research examines how transnational criminal organizations and cybercriminals may leverage developing AI technology to conduct more sophisticated criminal activities. Next, the research outlines the possible dynamic new kind of malware, called X-Ware and X-sWarm, which simulates the swarm system behaviour and integrates the neural network to operate more efficiently as a background for the forthcoming anti-malware solution. This research proposes how to record and visualize the behaviour of these type of malware when it propagates through the file system, computer network (virus process is known) or by observed data analysis (virus process is not known and we observe only the data from the system). Finally, a paradigm of an anti-malware solution, named Multi agent antivirus system has been proposed in the thesis that gives the insight to develop a more robust, adaptive and flexible defence system.Význam umělé inteligence (AI) a strojového učení (ML) v posledních letech rychle rostl a na jejich aplikacích lze vidět, že v mnoha oblastech, od rozpoznávání obličeje až po analýzu obrazu, byl učiněn velký pokrok. Poslední vývoj v oblasti umělé inteligence má obrovský potenciál jak pro obránce v oblasti kybernetické bezpečnosti, tak pro ůtočníky. AI se stává řešením v otázce obrany proti modernímu malware a hraje tak důležitou roli v detekci a prevenci hrozeb v digitálním prostoru. Naproti tomu kyberzločinci jsou si vědomi nových vyhlídek ve spojení s AI a pravděpodobně přizpůsobí tyto techniky novým generacím malware, vektorům útoku a celkově jejich operacím. Tato práce představuje dosavadní pokroky aplikace technik AI v oblasti kybernetické bezpečnosti. V této oblasti tzn. v boji proti kybernetickým hrozbám se ukázuje jako slibná technologie a užitečný nástroj pro detekci a prevenci kybernetických útoků. V práci si rovněž pokládme otázku, jak mohou nadnárodní zločinecké organizace a počítačoví zločinci využít vyvíjející se technologii umělé inteligence k provádění sofistikovanějších trestných činností. Konečně, výzkum nastíní možný nový druh malware, nazvaný X-Ware, který simuluje chování hejnového systému a integruje neuronovou síť tak, aby fungovala efektivněji a tak se celý X-Ware a X-sWarm dal použít nejen jako kybernetická zbraň na útok, ale i jako antivirové obranné řešení. Tento výzkum navrhuje, jak zaznamenat a vizualizovat chování X-Ware, když se šíří prostřednictvím systému souborů, sítí a to jak analýzou jeho dynamiky (proces je znám), tak analýzou dat (proces není znám, pozorujeme jen data). Nakonec bylo v disertační práci navrženo paradigma řešení proti malwaru, jež bylo nazváno „Multi agent antivirus system“. Tato práce tedy poskytuje pohled na vývoj robustnějšího, adaptivnějšího a flexibilnějšího obranného systému.460 - Katedra informatikyvyhově

Memetic algorithms for ontology alignment

2011 - 2012Semantic interoperability represents the capability of two or more systems to meaningfully and accurately interpret the exchanged data so as to produce useful results. It is an essential feature of all distributed and open knowledge based systems designed for both e-government and private businesses, since it enables machine interpretation, inferencing and computable logic. Unfortunately, the task of achieving semantic interoperability is very difficult because it requires that the meanings of any data must be specified in an appropriate detail in order to resolve any potential ambiguity. Currently, the best technology recognized for achieving such level of precision in specification of meaning is represented by ontologies. According to the most frequently referenced definition [1], an ontology is an explicit specification of a conceptualization, i.e., the formal specification of the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them [2]. However, different tasks or different points of view lead ontology designers to produce different conceptualizations of the same domain of interest. This means that the subjectivity of the ontology modeling results in the creation of heterogeneous ontologies characterized by terminological and conceptual discrepancies. Examples of these discrepancies are the use of different words to name the same concept, the use of the same word to name different concepts, the creation of hierarchies for a specific domain region with different levels of detail and so on. The arising so-called semantic heterogeneity problem represents, in turn, an obstacle for achieving semantic interoperability... [edited by author]XI n.s

Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks

Tesis por compendio[EN] The long-lasting trend in the field of computation of stress and resource distribution has found its way into computer networks via the concept of peer-to-peer (P2P) connectivity. P2P is a symmetrical model, where each network node is enabled a comparable range of capacities and resources. It stands in a stark contrast to the classical, strongly asymmetrical client-server approach. P2P, originally considered only a complimentary, server-side structure to the straightforward client-server model, has been shown to have the substantial potential on its own, with multiple, widely known benefits: good fault tolerance and recovery, satisfactory scalability and intrinsic load distribution. However, contrary to client-server, P2P networks require sophisticated solutions on all levels, ranging from network organization, to resource location and managing. In this thesis we address one of the key issues of P2P networks: performing efficient resource searches of semantic nature under realistic, dynamic conditions. There have been numerous solutions to this matter, with evolutionary, stigmergy-based, and simple computational foci, but few attempt to resolve the full range of challenges this problem entails. To name a few: real-life P2P networks are rarely static, nodes disconnect, reconnect and change their content. In addition, a trivial incorporation of semantic searches into well-known algorithms causes significant decrease in search efficiency. In our research we build a solution incrementally, starting with the classic Ant Colony System (ACS) within the Ant Colony Optimization metaheuristic (ACO). ACO is an algorithmic framework used for solving combinatorial optimization problems that fits contractually the problem very well, albeit not providing an immediate solution to any of the aforementioned problems. First, we propose an efficient ACS variant in structured (hypercube structured) P2P networks, by enabling a path-post processing algorithm, which called Tabu Route Optimization (TRO). Next, we proceed to resolve the issue of network dynamism with an ACO-compatible information diffusion approach. Consequently, we attempt to incorporate the semantic component of the searches. This initial approximation to the problem was achieved by allowing ACS to differentiate between search types with the pheromone-per-concept idea. We called the outcome of this merger Routing Concept ACS (RC-ACS). RC-ACS is a robust, static multipheromone implementation of ACS. However, we were able to conclude from it that the pheromone-per-concept approach offers only limited scalability and cannot be considered a global solution. Thus, further progress was made in this respect when we introduced to RC-ACS our novel idea: dynamic pheromone creation, which replaces the static one-to-one assignment. We called the resulting algorithm Angry Ant Framework (AAF). In AAF new pheromone levels are created as needed and during the search, rather than prior to it. The final step was to enable AAF, not only to create pheromone levels, but to reassign them to optimize the pheromone usage. The resulting algorithm is called EntropicAAF and it has been evaluated as one of the top-performing algorithms for P2P semantic searches under all conditions.[ES] La popular tendencia de distribución de carga y recursos en el ámbito de la computación se ha transmitido a las redes computacionales a través del concepto de la conectividad peer-to-peer (P2P). P2P es un modelo simétrico, en el cual a cada nodo de la red se le otorga un rango comparable de capacidades y recursos. Se trata de un fuerte contraste con el clásico y fuertemente asimétrico enfoque cliente-servidor. P2P, originalmente considerado solo como una estructura del lado del servidor complementaria al sencillo modelo cliente-servidor, ha demostrado tener un potencial considerable por sí mismo, con múltiples beneficios ampliamente conocidos: buena tolerancia a fallos y recuperación, escalabilidad satisfactoria y distribución de carga intrínseca. Sin embargo, al contrario que el modelo cliente-servidor, las redes P2P requieren de soluciones sofisticadas a todos los niveles, desde la organización de la red hasta la gestión y localización de recursos. Esta tesis aborda uno de los problemas principales de las redes P2P: la búsqueda eficiente de recursos de naturaleza semántica bajo condiciones dinámicas y realistas. Ha habido numerosas soluciones a este problema basadas en enfoques evolucionarios, estigmérgicos y simples, pero pocas han tratado de resolver el abanico completo de desafíos. En primer lugar, las redes P2P reales son raramente estáticas: los nodos se desconectan, reconectan y cambian de contenido. Además, la incorporación trivial de búsquedas semánticas en algoritmos conocidos causa un decremento significativo de la eficiencia de la búsqueda. En esta investigación se ha construido una solución de manera incremental, comenzando por el clásico Ant Colony System (ACS) basado en la metaheurística de Ant Colony Optimization (ACO). ACO es un framework algorítmico usado para búsquedas en grafos que encaja perfectamente con las condiciones del problema, aunque no provee una solución inmediata a las cuestiones mencionadas anteriormente. En primer lugar, se propone una variante eficiente de ACS para redes P2P estructuradas (con estructura de hipercubo) permitiendo el postprocesamiento de las rutas, al que hemos denominado Tabu Route Optimization (TRO). A continuación, se ha tratado de resolver el problema del dinamismo de la red mediante la difusión de la información a través de una estrategia compatible con ACO. En consecuencia, se ha tratado de incorporar el componente semántico de las búsquedas. Esta aproximación inicial al problema ha sido lograda permitiendo al ACS diferenciar entre tipos de búsquedas através de la idea de pheromone-per-concept. El resultado de esta fusión se ha denominado Routing Concept ACS (RC-ACS). RC-ACS es una implementación multiferomona estática y robusta de ACS. Sin embargo, a partir de esta implementación se ha podido concluir que el enfoque pheromone-per-concept ofrece solo escalabilidad limitada y que no puede ser considerado una solución global. Por lo tanto, para lograr una mejora a este respecto, se ha introducido al RC-ACS una novedosa idea: la creación dinámica de feromonas, que reemplaza la asignación estática uno a uno. En el algoritmo resultante, al que hemos denominado Angry Ant Framework (AAF), los nuevos niveles de feromona se crean conforme se necesitan y durante la búsqueda, en lugar de crearse antes de la misma. La mejora final se ha obtenido al permitir al AAF no solo crear niveles de feromona, sino también reasignarlos para optimizar el uso de la misma. El algoritmo resultante se denomina EntropicAAF y ha sido evaluado como uno de los algoritmos más exitosos para las búsquedas semánticas P2P bajo todas las condiciones.[CA] La popular tendència de distribuir càrrega i recursos en el camp de la computació s'ha estès cap a les xarxes d'ordinadors a través del concepte de connexions d'igual a igual (de l'anglès, peer to peer o P2P). P2P és un model simètric on cada node de la xarxa disposa del mateix nombre de capacitats i recursos. P2P, considerat originàriament només una estructura situada al servidor complementària al model client-servidor simple, ha provat tindre el suficient potencial per ella mateixa, amb múltiples beneficis ben coneguts: una bona tolerància a errades i recuperació, una satisfactòria escalabilitat i una intrínseca distribució de càrrega. No obstant, contràriament al client-servidor, les xarxes P2P requereixen solucions sofisticades a tots els nivells, que varien des de l'organització de la xarxa a la localització de recursos i la seua gestió. En aquesta tesi s'adreça un dels problemes clau de les xarxes P2P: ser capaç de realitzar eficientment cerques de recursos de naturalesa semàntica sota condicions realistes i dinàmiques. Existeixen nombroses solucions a aquest tema basades en la computació simple, evolutiva i també basades en l'estimèrgia (de l'anglès, stigmergy), però pocs esforços s'han realitzat per intentar resoldre l'ampli conjunt de reptes existent. En primer lloc, les xarxes P2P reals són rarament estàtiques: els nodes es connecten, desconnecten i canvien els seus continguts. A més a més, la incorporació trivial de cerques semàntiques als algorismes existents causa una disminució significant de l'eficiència de la cerca. En aquesta recerca s'ha construït una solució incremental, començant pel sistema clàssic de colònia de formigues (de l'anglés, Ant Colony System o ACS) dins de la metaheurística d'optimització de colònies de formigues (de l'anglès, Ant Colony Optimization o ACO). ACO és un entorn algorísmic utilitzat per cercar en grafs i que aborda el problema de forma satisfactòria, tot i que no proveeix d'una solució immediata a cap dels problemes anteriorment mencionats. Primer, s'ha proposat una variant eficient d'ACS en xarxes P2P estructurades (en forma d'hipercub) a través d'un algorisme de processament post-camí el qual s'ha anomenat en anglès Tabu Route Optimization (TRO). A continuació, s'ha procedit a resoldre el problema del dinamisme de les xarxes amb un enfocament de difusió d'informació compatible amb ACO. Com a conseqüència, s'ha intentat incorporar la component semàntica de les cerques. Aquest enfocament inicial al problema s'ha realitzat permetent a ACS diferenciar entre tipus de cerques amb la idea de ''feromona per concepte'', i s'ha anomenat a aquest producte Routing Concept ACS o RC-ACS. RC-ACS és una implementació multi-feromona robusta i estàtica d'ACS. No obstant, s'ha pogut concloure que l'enfocament de feromona per concepte ofereix només una escalabilitat limitada i no pot ser considerada una solució global. En aquest respecte s'ha realitzat progrés posteriorment introduint una nova idea a RC-ACS: la creació dinàmica de feromones, la qual reemplaça a l'assignació un a un de les mateixes. A l'algorisme resultant se l'ha anomenat en anglès Angry Ant Framework (AAF). En AAF es creen nous nivells de feromones a mesura que es necessiten durant la cerca, i no abans d'aquesta. El progrés final s'ha aconseguit quan s'ha permès a AAF, no sols crear nivells de feromones, sinó reassignar-los per optimitzar la utilització de feromones. L'algorisme resultant s'ha anomenat EntropicAAF i ha sigut avaluat com un dels algorismes per a cerques semàntiques P2P amb millors prestacions.Krynicki, KK. (2016). Ant Colony Algorithms for the Resolution of Semantic Searches in P2P Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/61293TESISPremios Extraordinarios de tesis doctoralesCompendi

Modélisation formelle des systèmes de détection d'intrusions

L’écosystème de la cybersécurité évolue en permanence en termes du nombre, de la diversité, et de la complexité des attaques. De ce fait, les outils de détection deviennent inefficaces face à certaines attaques. On distingue généralement trois types de systèmes de détection d’intrusions : détection par anomalies, détection par signatures et détection hybride. La détection par anomalies est fondée sur la caractérisation du comportement habituel du système, typiquement de manière statistique. Elle permet de détecter des attaques connues ou inconnues, mais génère aussi un très grand nombre de faux positifs. La détection par signatures permet de détecter des attaques connues en définissant des règles qui décrivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La détection hybride repose sur plusieurs méthodes de détection incluant celles sus-citées. Elle présente l’avantage d’être plus précise pendant la détection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de règles de reconnaissance d’attaques. Le nombre d’attaques potentielles étant très grand, ces bases de règles deviennent rapidement difficiles à gérer et à maintenir. De plus, l’expression de règles avec état dit stateful est particulièrement ardue pour reconnaître une séquence d’événements. Dans cette thèse, nous proposons une approche stateful basée sur les diagrammes d’état-transition algébriques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de représenter de façon graphique et modulaire une spécification, ce qui facilite la maintenance et la compréhension des règles. Nous étendons la notation ASTD avec de nouvelles fonctionnalités pour représenter des attaques complexes. Ensuite, nous spécifions plusieurs attaques avec la notation étendue et exécutons les spécifications obtenues sur des flots d’événements à l’aide d’un interpréteur pour identifier des attaques. Nous évaluons aussi les performances de l’interpréteur avec des outils industriels tels que Snort et Zeek. Puis, nous réalisons un compilateur afin de générer du code exécutable à partir d’une spécification ASTD, capable d’identifier de façon efficiente les séquences d’événements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity, and the complexity of cyber attacks. Generally, we have three types of Intrusion Detection System (IDS) : anomaly-based detection, signature-based detection, and hybrid detection. Anomaly detection is based on the usual behavior description of the system, typically in a static manner. It enables detecting known or unknown attacks but also generating a large number of false positives. Signature based detection enables detecting known attacks by defining rules that describe known attacker’s behavior. It needs a good knowledge of attacker behavior. Hybrid detection relies on several detection methods including the previous ones. It has the advantage of being more precise during detection. Tools like Snort and Zeek offer low level languages to represent rules for detecting attacks. The number of potential attacks being large, these rule bases become quickly hard to manage and maintain. Moreover, the representation of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular representation of a specification, that facilitates maintenance and understanding of rules. We extend the ASTD notation with new features to represent complex attacks. Next, we specify several attacks with the extended notation and run the resulting specifications on event streams using an interpreter to identify attacks. We also evaluate the performance of the interpreter with industrial tools such as Snort and Zeek. Then, we build a compiler in order to generate executable code from an ASTD specification, able to efficiently identify sequences of events

Simulating social relations in multi-agent systems

Open distributed systems are comprised of a large number of heterogeneous nodes with disparate requirements and objectives, a number of which may not conform to the system specification. This thesis argues that activity in such systems can be regulated by using distributed mechanisms inspired by social science theories regarding similarity /kinship, trust, reputation, recommendation and economics. This makes it possible to create scalable and robust agent societies which can adapt to overcome structural impediments and provide inherent defence against malicious and incompetent action, without detriment to system functionality and performance. In particular this thesis describes: • an agent based simulation and animation platform (PreSage), which offers the agent developer and society designer a suite of powerful tools for creating, simulating and visualising agent societies from both a local and global perspective. • a social information dissemination system (SID) based on principles of self organisation which personalises recommendation and directs information dissemination. • a computational socio-cognitive and economic framework (CScEF) which integrates and extends socio-cognitive theories of trust, reputation and recommendation with basic economic theory. • results from two simulation studies investigating the performance of SID and the CScEF. The results show the production of a generic, reusable and scalable platform for developing and animating agent societies, and its contribution to the community as an open source tool. Secondly specific results, regarding the application of SID and CScEF, show that revealing outcomes of using socio-technical mechanisms to condition agent interactions can be demonstrated and identified by using Presage.Open Acces

Libro de Actas JCC&BD 2018 : VI Jornadas de Cloud Computing & Big Data

Se recopilan las ponencias presentadas en las VI Jornadas de Cloud Computing & Big Data (JCC&BD), realizadas entre el 25 al 29 de junio de 2018 en la Facultad de Informática de la Universidad Nacional de La Plata.Universidad Nacional de La Plata (UNLP) - Facultad de Informátic

Evolutionary Learning of Goal-Driven Multi-agent Communication

Multi-agent systems are a common paradigm for building distributed systems in different domains such as networking, health care, swarm sensing, robotics, and transportation. Systems are usually designed or adjusted in order to reflect the performance trade-offs made according to the characteristics of the mission requirement. Research has acknowledged the crucial role that communication plays in solving many performance problems. Conversely, research efforts that address communication decisions are usually designed and evaluated with respect to a single predetermined performance goal. This work introduces Goal-Driven Communication, where communication in a multi-agent system is determined according to flexible performance goals. This work proposes an evolutionary approach that, given a performance goal, produces a communication strategy that can improve a multi-agent system's performance with respect to the desired goal. The evolved strategy determines what, when, and to whom the agents communicate. The proposed approach further enables tuning the trade-off between the performance goal and communication cost, to produce a strategy that achieves a good balance between the two objectives, according the system designer's needs