78 research outputs found

    DIDS Using Cooperative Agents Based on Ant Colony Clustering

    Get PDF
    Intrusion detection systems (IDS) play an important role in information security. Two major problems in the development of IDSs are the computational aspect and the architectural aspect. The computational or algorithmic problems include lacking ability of novel-attack detection and computation overload caused by large data traffic. The architectural problems are related to the communication between components of detection, including difficulties to overcome distributed and coordinated attacks because of the need of large amounts of distributed information and synchronization between detection components. This paper proposes a multi-agent architecture for a distributed intrusion detection system (DIDS) based on ant-colony clustering (ACC), for recognizing new and coordinated attacks, handling large data traffic, synchronization, co-operation between components without the presence of centralized computation, and good detection performance in real-time with immediate alarm notification. Feature selection based on principal component analysis (PCA) is used for dimensional reduction of NSL-KDD. Initial features are transformed to new features in smaller dimensions, where probing attacks (Ra-Probe) have a characteristic sign in their average value that is different from that of normal activity. Selection is based on the characteristics of these factors, resulting in a two-dimensional subset of the 75% data reduction

    Machine Learning for Unmanned Aerial System (UAS) Networking

    Get PDF
    Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

    Hybridization of machine learning for advanced manufacturing

    Get PDF
    Tesis por compendio de publicacioines[ES] En el contexto de la industria, hoy por hoy, los términos “Fabricación Avanzada”, “Industria 4.0” y “Fábrica Inteligente” están convirtiéndose en una realidad. Las empresas industriales buscan ser más competitivas, ya sea en costes, tiempo, consumo de materias primas, energía, etc. Se busca ser eficiente en todos los ámbitos y además ser sostenible. El futuro de muchas compañías depende de su grado de adaptación a los cambios y su capacidad de innovación. Los consumidores son cada vez más exigentes, buscando productos personalizados y específicos con alta calidad, a un bajo coste y no contaminantes. Por todo ello, las empresas industriales implantan innovaciones tecnológicas para conseguirlo. Entre estas innovaciones tecnológicas están la ya mencionada Fabricación Avanzada (Advanced Manufacturing) y el Machine Learning (ML). En estos campos se enmarca el presente trabajo de investigación, en el que se han concebido y aplicado soluciones inteligentes híbridas que combinan diversas técnicas de ML para resolver problemas en el campo de la industria manufacturera. Se han aplicado técnicas inteligentes tales como Redes Neuronales Artificiales (RNA), algoritmos genéticos multiobjetivo, métodos proyeccionistas para la reducción de la dimensionalidad, técnicas de agrupamiento o clustering, etc. También se han utilizado técnicas de Identificación de Sistemas con el propósito de obtener el modelo matemático que representa mejor el sistema real bajo estudio. Se han hibridado diversas técnicas con el propósito de construir soluciones más robustas y fiables. Combinando técnicas de ML específicas se crean sistemas más complejos y con una mayor capacidad de representación/solución. Estos sistemas utilizan datos y el conocimiento sobre estos para resolver problemas. Las soluciones propuestas buscan solucionar problemas complejos del mundo real y de un amplio espectro, manejando aspectos como la incertidumbre, la falta de precisión, la alta dimensionalidad, etc. La presente tesis cubre varios casos de estudio reales, en los que se han aplicado diversas técnicas de ML a distintas problemáticas del campo de la industria manufacturera. Los casos de estudio reales de la industria en los que se ha trabajado, con cuatro conjuntos de datos diferentes, se corresponden con: • Proceso de fresado dental de alta precisión, de la empresa Estudio Previo SL. • Análisis de datos para el mantenimiento predictivo de una empresa del sector de la automoción, como es la multinacional Grupo Antolin. Adicionalmente se ha colaborado con el grupo de investigación GICAP de la Universidad de Burgos y con el centro tecnológico ITCL en los casos de estudio que forman parte de esta tesis y otros relacionados. Las diferentes hibridaciones de técnicas de ML desarrolladas han sido aplicadas y validadas con conjuntos de datos reales y originales, en colaboración con empresas industriales o centros de fresado, permitiendo resolver problemas actuales y complejos. De esta manera, el trabajo realizado no ha tenido sólo un enfoque teórico, sino que se ha aplicado de modo práctico permitiendo que las empresas industriales puedan mejorar sus procesos, ahorrar en costes y tiempo, contaminar menos, etc. Los satisfactorios resultados obtenidos apuntan hacia la utilidad y aportación que las técnicas de ML pueden realizar en el campo de la Fabricación Avanzada

    Free-text keystroke dynamics authentication with a reduced need for training and language independency

    Get PDF
    This research aims to overcome the drawback of the large amount of training data required for free-text keystroke dynamics authentication. A new key-pairing method, which is based on the keyboard’s key-layout, has been suggested to achieve that. The method extracts several timing features from specific key-pairs. The level of similarity between a user’s profile data and his or her test data is then used to decide whether the test data was provided by the genuine user. The key-pairing technique was developed to use the smallest amount of training data in the best way possible which reduces the requirement for typing long text in the training stage. In addition, non-conventional features were also defined and extracted from the input stream typed by the user in order to understand more of the users typing behaviours. This helps the system to assemble a better idea about the user’s identity from the smallest amount of training data. Non-conventional features compute the average of users performing certain actions when typing a whole piece of text. Results were obtained from the tests conducted on each of the key-pair timing features and the non-conventional features, separately. An FAR of 0.013, 0.0104 and an FRR of 0.384, 0.25 were produced by the timing features and non-conventional features, respectively. Moreover, the fusion of these two feature sets was utilized to enhance the error rates. The feature-level fusion thrived to reduce the error rates to an FAR of 0.00896 and an FRR of 0.215 whilst decision-level fusion succeeded in achieving zero FAR and FRR. In addition, keystroke dynamics research suffers from the fact that almost all text included in the studies is typed in English. Nevertheless, the key-pairing method has the advantage of being language-independent. This allows for it to be applied on text typed in other languages. In this research, the key-pairing method was applied to text in Arabic. The results produced from the test conducted on Arabic text were similar to those produced from English text. This proves the applicability of the key-pairing method on a language other than English even if that language has a completely different alphabet and characteristics. Moreover, experimenting with texts in English and Arabic produced results showing a direct relation between the users’ familiarity with the language and the performance of the authentication system

    Computer Science & Technology Series : XVI Argentine Congress of Computer Science - Selected papers

    Get PDF
    CACIC’10 was the sixteenth Congress in the CACIC series. It was organized by the School of Computer Science of the University of Moron. The Congress included 10 Workshops with 104 accepted papers, 1 main Conference, 4 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. (http://www.cacic2010.edu.ar/). CACIC 2010 was organized following the traditional Congress format, with 10 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of three chairs of different Universities. The call for papers attracted a total of 195 submissions. An average of 2.6 review reports were collected for each paper, for a grand total of 507 review reports that involved about 300 different reviewers. A total of 104 full papers were accepted and 20 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

    Technology, Science and Culture

    Get PDF
    From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them

    A methodology for the quantitative evaluation of attacks and mitigations in IoT systems

    Get PDF
    PhD ThesisAs we move towards a more distributed and unsupervised internet, namely through the Internet of Things (IoT), the avenues of attack multiply. To compound these issues, whilst attacks are developing, the current security of devices is much lower than for traditional systems. In this thesis I propose a new methodology for white box behaviour intrusion detection in constrained systems. I leverage the characteristics of these types of systems, namely their: heterogeneity, distributed nature, and constrained capabilities; to devise a pipeline, that given a specification of a IoT scenario can generate an actionable intrusion detection system to protect it. I identify key IoT scenarios for which more traditional black box approaches would not suffice, and devise means to bypass these limitations. The contributions include; 1) A survey of intrusion detection for IoT; 2) A modelling technique to observe interactions in IoT deployments; 3) A modelling approach that focuses on the observation of specific attacks on possible configurations of IoT devices; Combining these components: a specification of the system as per contribution 1 and a attack specification as per contribution 2, we can deploy a bespoke behaviour based IDS for the specified system. This one of a kind approach allows for the quick and efficient generation of attack detection from the onset, positioning this approach as particularly suitable to dynamic and constrained IoT environments

    Enabling sustainable power distribution networks by using smart grid communications

    Get PDF
    Smart grid modernization enables integration of computing, information and communications capabilities into the legacy electric power grid system, especially the low voltage distribution networks where various consumers are located. The evolutionary paradigm has initiated worldwide deployment of an enormous number of smart meters as well as renewable energy sources at end-user levels. The future distribution networks as part of advanced metering infrastructure (AMI) will involve decentralized power control operations under associated smart grid communications networks. This dissertation addresses three potential problems anticipated in the future distribution networks of smart grid: 1) local power congestion due to power surpluses produced by PV solar units in a neighborhood that demands disconnection/reconnection mechanisms to alleviate power overflow, 2) power balance associated with renewable energy utilization as well as data traffic across a multi-layered distribution network that requires decentralized designs to facilitate power control as well as communications, and 3) a breach of data integrity attributed to a typical false data injection attack in a smart metering network that calls for a hybrid intrusion detection system to detect anomalous/malicious activities. In the first problem, a model for the disconnection process via smart metering communications between smart meters and the utility control center is proposed. By modeling the power surplus congestion issue as a knapsack problem, greedy solutions for solving such problem are proposed. Simulation results and analysis show that computation time and data traffic under a disconnection stage in the network can be reduced. In the second problem, autonomous distribution networks are designed that take scalability into account by dividing the legacy distribution network into a set of subnetworks. A power-control method is proposed to tackle the power flow and power balance issues. Meanwhile, an overlay multi-tier communications infrastructure for the underlying power network is proposed to analyze the traffic of data information and control messages required for the associated power flow operations. Simulation results and analysis show that utilization of renewable energy production can be improved, and at the same time data traffic reduction under decentralized operations can be achieved as compared to legacy centralized management. In the third problem, an attack model is proposed that aims to minimize the number of compromised meters subject to the equality of an aggregated power load in order to bypass detection under the conventionally radial tree-like distribution network. A hybrid anomaly detection framework is developed, which incorporates the proposed grid sensor placement algorithm with the observability attribute. Simulation results and analysis show that the network observability as well as detection accuracy can be improved by utilizing grid-placed sensors. Conclusively, a number of future works have also been identified to furthering the associated problems and proposed solutions

    The evolution and mechanisms of caste plasticity in vespid wasps

    Get PDF
    Social insects are ecologically dominant predators, pollinators, herbivores and detritivores across many terrestrial ecosystems. Key to the ecological success of these species is a uniquely strong division of labour between reproductives (‘queens’) and non-reproductives (‘workers’). In some social insect species, reproductive division of labour is obligate and developmentally determined, but many other taxa possess full reproductive plasticity, which is the basal state for social insect evolution. Answering the question of how division of reproductive labour is maintained in the presence of reproductive plasticity is an important prerequisite to understanding how and why this plasticity has been lost in the most derived social insect taxa. In this thesis, I address this question using two species of social wasp which exhibit strong division of reproductive labour but full reproductive plasticity. Two chapters of the thesis examine responses to queen loss in the European paper wasp P. dominula, in order to understand the mechanisms by which groups accommodate the loss of a reproductive. In Chapter 2 I show that in this species, groups generate replacement reproductives rapidly and with little conflict by relying on an age-based succession criterion. In Chapter 3 I analyse the transcriptomic mechanisms that underlie this succession process, and show that variation in individuals’ phenotypes only partially explains their transcriptomic responses, a result that suggests hidden costs of queen loss. In Chapter 4, I analyse individual-level transcriptomic data from a facultatively social tropical hover wasp, Liostenogaster flavolineata, which forms linearly age-based dominance hierarchies in which individuals exhibit progressively reduced foraging effort as they move up in rank. I show that despite differences in social structure, variation in gene expression in colonies of this species is surprisingly similar to that of obligately social species such as P. dominula. I also find that genes that are associated with indirect fitness in L. flavolineata are more strongly evolutionarily conserved than genes associated with direct fitness, a surprising result that runs counter to results obtained for other social insect species. Additionally, in Chapter 5 I argue for a reconceptualization of the loss of reproductive plasticity that has occurred in more complex insect societies. Taken as a whole, this thesis sheds light on the behavioural and transcriptomic mechanisms by which distinct fitness strategies are maintained in reproductively skewed societies as well as revealing potential limitations of these mechanisms, emphasising the value of reproductively plastic social insects as models for the evolution of sociality

    Co-evolutionary Hybrid Bi-level Optimization

    Get PDF
    Multi-level optimization stems from the need to tackle complex problems involving multiple decision makers. Two-level optimization, referred as ``Bi-level optimization'', occurs when two decision makers only control part of the decision variables but impact each other (e.g., objective value, feasibility). Bi-level problems are sequential by nature and can be represented as nested optimization problems in which one problem (the ``upper-level'') is constrained by another one (the ``lower-level''). The nested structure is a real obstacle that can be highly time consuming when the lower-level is NPhard\mathcal{NP}-hard. Consequently, classical nested optimization should be avoided. Some surrogate-based approaches have been proposed to approximate the lower-level objective value function (or variables) to reduce the number of times the lower-level is globally optimized. Unfortunately, such a methodology is not applicable for large-scale and combinatorial bi-level problems. After a deep study of theoretical properties and a survey of the existing applications being bi-level by nature, problems which can benefit from a bi-level reformulation are investigated. A first contribution of this work has been to propose a novel bi-level clustering approach. Extending the well-know ``uncapacitated k-median problem'', it has been shown that clustering can be easily modeled as a two-level optimization problem using decomposition techniques. The resulting two-level problem is then turned into a bi-level problem offering the possibility to combine distance metrics in a hierarchical manner. The novel bi-level clustering problem has a very interesting property that enable us to tackle it with classical nested approaches. Indeed, its lower-level problem can be solved in polynomial time. In cooperation with the Luxembourg Centre for Systems Biomedicine (LCSB), this new clustering model has been applied on real datasets such as disease maps (e.g. Parkinson, Alzheimer). Using a novel hybrid and parallel genetic algorithm as optimization approach, the results obtained after a campaign of experiments have the ability to produce new knowledge compared to classical clustering techniques combining distance metrics in a classical manner. The previous bi-level clustering model has the advantage that the lower-level can be solved in polynomial time although the global problem is by definition NP\mathcal{NP}-hard. Therefore, next investigations have been undertaken to tackle more general bi-level problems in which the lower-level problem does not present any specific advantageous properties. Since the lower-level problem can be very expensive to solve, the focus has been turned to surrogate-based approaches and hyper-parameter optimization techniques with the aim of approximating the lower-level problem and reduce the number of global lower-level optimizations. Adapting the well-know bayesian optimization algorithm to solve general bi-level problems, the expensive lower-level optimizations have been dramatically reduced while obtaining very accurate solutions. The resulting solutions and the number of spared lower-level optimizations have been compared to the bi-level evolutionary algorithm based on quadratic approximations (BLEAQ) results after a campaign of experiments on official bi-level benchmarks. Although both approaches are very accurate, the bi-level bayesian version required less lower-level objective function calls. Surrogate-based approaches are restricted to small-scale and continuous bi-level problems although many real applications are combinatorial by nature. As for continuous problems, a study has been performed to apply some machine learning strategies. Instead of approximating the lower-level solution value, new approximation algorithms for the discrete/combinatorial case have been designed. Using the principle employed in GP hyper-heuristics, heuristics are trained in order to tackle efficiently the NPhard\mathcal{NP}-hard lower-level of bi-level problems. This automatic generation of heuristics permits to break the nested structure into two separated phases: \emph{training lower-level heuristics} and \emph{solving the upper-level problem with the new heuristics}. At this occasion, a second modeling contribution has been introduced through a novel large-scale and mixed-integer bi-level problem dealing with pricing in the cloud, i.e., the Bi-level Cloud Pricing Optimization Problem (BCPOP). After a series of experiments that consisted in training heuristics on various lower-level instances of the BCPOP and using them to tackle the bi-level problem itself, the obtained results are compared to the ``cooperative coevolutionary algorithm for bi-level optimization'' (COBRA). Although training heuristics enables to \emph{break the nested structure}, a two phase optimization is still required. Therefore, the emphasis has been put on training heuristics while optimizing the upper-level problem using competitive co-evolution. Instead of adopting the classical decomposition scheme as done by COBRA which suffers from the strong epistatic links between lower-level and upper-level variables, co-evolving the solution and the mean to get to it can cope with these epistatic link issues. The ``CARBON'' algorithm developed in this thesis is a competitive and hybrid co-evolutionary algorithm designed for this purpose. In order to validate the potential of CARBON, numerical experiments have been designed and results have been compared to state-of-the-art algorithms. These results demonstrate that ``CARBON'' makes possible to address nested optimization efficiently
    corecore