9,299 research outputs found
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
Evolutionary Synthesis of Analog Electronic Circuits Using EDA Algorithms
DisertaÄnĂ prĂĄce je zamÄĹena na nĂĄvrh analogovĂ˝ch elektronickĂ˝ch obvodĹŻ pomocĂ algoritmĹŻ s pravÄpodobnostnĂmi modely (algoritmy EDA). PrezentovanĂŠ metody jsou na zĂĄkladÄ poĹžadovanĂ˝ch charakteristik cĂlovĂ˝ch obvodĹŻ schopny navrhnout jak parametry pouĹžitĂ˝ch komponent tak takĂŠ jejich topologii zapojenĂ. TĹi rĹŻznĂŠ metody vyuĹžitĂ EDA algoritmĹŻ jsou navrĹženy a otestovĂĄny na pĹĂkladech skuteÄnĂ˝ch problĂŠmĹŻ z oblasti analogovĂ˝ch elektronickĂ˝ch obvodĹŻ. PrvnĂ metoda je urÄena pro nĂĄvrh pasivnĂch analogovĂ˝ch obvodĹŻ a vyuĹžĂvĂĄ algoritmus UMDA pro nĂĄvrh jak topologie zapojenĂ tak takĂŠ hodnot parametrĹŻ pouĹžitĂ˝ch komponent. Metoda je pouĹžita pro nĂĄvrh admitanÄnĂ sĂtÄ s poĹžadovanou vstupnĂ impedancĂ pro ĂşÄely chaotickĂŠho oscilĂĄtoru. DruhĂĄ metoda je takĂŠ urÄena pro nĂĄvrh pasivnĂch analogovĂ˝ch obvodĹŻ a vyuĹžĂvĂĄ hybridnĂ pĹĂstup - UMDA pro nĂĄvrh topologie a metodu lokĂĄlnĂ optimalizace pro nĂĄvrh parametrĹŻ komponent. TĹetĂ metoda umoĹžĹuje nĂĄvrh analogovĂ˝ch obvodĹŻ obsahujĂcĂch takĂŠ tranzistory. Metoda vyuĹžĂvĂĄ hybridnĂ pĹĂstup - EDA algoritmus pro syntĂŠzu topologie a metoda lokĂĄlnĂ optimalizace pro urÄenĂ parametrĹŻ pouĹžitĂ˝ch komponent. Informace o topologii je v jednotlivĂ˝ch jedincĂch populace vyjĂĄdĹena pomocĂ grafĹŻ a hypergrafĹŻ.Dissertation thesis is focused on design of analog electronic circuits using Estimation of Distribution Algorithms (EDA). Based on the desired characteristics of the target circuits the proposed methods are able to design the parameters of the used components and theirs topology of connection as well. Three different methods employing EDA algorithms are proposed and verified on examples of real problems from the area of analog circuits design. The first method is capable to design passive analog circuits. The method employs UMDA algorithm which is used for determination of the parameters of the used components and synthesis of the topology of their connection as well. The method is verified on the problem of design of admittance network with desired input impedance function which is used as a part of chaotic oscillator circuit. The second method is also capable to design passive analog circuits. The method employs hybrid approach - UMDA for synthesis of the topology and local optimization method for determination of the parameters of the components. The third method is capable to design analog circuits which include also ac- tive components such as transistors. Hybrid approach is used. The topology is synthesized using EDA algorithm and the parameters are determined using a local optimization method. In the individuals of the population information about the topology is represented using graphs and hypergraphs.
A general framework of multi-population methods with clustering in undetectable dynamic environments
Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple
population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g.,
how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this
paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental
results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks
benchmark
Reinforcement learning based local search for grouping problems: A case study on graph coloring
Grouping problems aim to partition a set of items into multiple mutually
disjoint subsets according to some specific criterion and constraints. Grouping
problems cover a large class of important combinatorial optimization problems
that are generally computationally difficult. In this paper, we propose a
general solution approach for grouping problems, i.e., reinforcement learning
based local search (RLS), which combines reinforcement learning techniques with
descent-based local search. The viability of the proposed approach is verified
on a well-known representative grouping problem (graph coloring) where a very
simple descent-based coloring algorithm is applied. Experimental studies on
popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves
competitive performances compared to a number of well-known coloring
algorithms
Recovering complete and draft population genomes from metagenome datasets.
Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution
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