Machine learning for network based intrusion detection: an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data.

For the last decade it has become commonplace to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of date. Therefore, some researchers question the validity of the findings reported based on this data set. Furthermore, as identified in this thesis, there are also discrepancies in the findings reported in the literature. In some cases the results are contradictory. Consequently, it is difficult to analyse the current body of research to determine the value in the findings. This thesis reports on an empirical investigation to determine the underlying causes of the discrepancies. Several methodological factors, such as choice of data subset, validation method and data preprocessing, are identified and are found to affect the results significantly. These findings have also enabled a better interpretation of the current body of research. Furthermore, the criticisms in the literature are addressed and future use of the data set is discussed, which is important since researchers continue to use it due to a lack of better publicly available alternatives. Due to the nature of the intrusion detection domain, there is an extreme imbalance among the classes in the KDD Cup '99 data set, which poses a significant challenge to machine learning. In other domains, researchers have demonstrated that well known techniques such as Artificial Neural Networks (ANNs) and Decision Trees (DTs) often fail to learn the minor class(es) due to class imbalance. However, this has not been recognized as an issue in intrusion detection previously. This thesis reports on an empirical investigation that demonstrates that it is the class imbalance that causes the poor detection of some classes of intrusion reported in the literature. An alternative approach to training ANNs is proposed in this thesis, using Genetic Algorithms (GAs) to evolve the weights of the ANNs, referred to as an Evolutionary Neural Network (ENN). When employing evaluation functions that calculate the fitness proportionally to the instances of each class, thereby avoiding a bias towards the major class(es) in the data set, significantly improved true positive rates are obtained whilst maintaining a low false positive rate. These findings demonstrate that the issues of learning from imbalanced data are not due to limitations of the ANNs; rather the training algorithm. Moreover, the ENN is capable of detecting a class of intrusion that has been reported in the literature to be undetectable by ANNs. One limitation of the ENN is a lack of control of the classification trade-off the ANNs obtain. This is identified as a general issue with current approaches to creating classifiers. Striving to create a single best classifier that obtains the highest accuracy may give an unfruitful classification trade-off, which is demonstrated clearly in this thesis. Therefore, an extension of the ENN is proposed, using a Multi-Objective GA (MOGA), which treats the classification rate on each class as a separate objective. This approach produces a Pareto front of non-dominated solutions that exhibit different classification trade-offs, from which the user can select one with the desired properties. The multi-objective approach is also utilised to evolve classifier ensembles, which yields an improved Pareto front of solutions. Furthermore, the selection of classifier members for the ensembles is investigated, demonstrating how this affects the performance of the resultant ensembles. This is a key to explaining why some classifier combinations fail to give fruitful solutions

Development of 2D- and 3D-BTEM for pattern recognition in higher-order spectroscopic and other data arrays

Ph.DDOCTOR OF PHILOSOPH

Machine learning for network based intrusion detection : an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data

For the last decade it has become commonplace to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of date. Therefore, some researchers question the validity of the findings reported based on this data set. Furthermore, as identified in this thesis, there are also discrepancies in the findings reported in the literature. In some cases the results are contradictory. Consequently, it is difficult to analyse the current body of research to determine the value in the findings. This thesis reports on an empirical investigation to determine the underlying causes of the discrepancies. Several methodological factors, such as choice of data subset, validation method and data preprocessing, are identified and are found to affect the results significantly. These findings have also enabled a better interpretation of the current body of research. Furthermore, the criticisms in the literature are addressed and future use of the data set is discussed, which is important since researchers continue to use it due to a lack of better publicly available alternatives. Due to the nature of the intrusion detection domain, there is an extreme imbalance among the classes in the KDD Cup '99 data set, which poses a significant challenge to machine learning. In other domains, researchers have demonstrated that well known techniques such as Artificial Neural Networks (ANNs) and Decision Trees (DTs) often fail to learn the minor class(es) due to class imbalance. However, this has not been recognized as an issue in intrusion detection previously. This thesis reports on an empirical investigation that demonstrates that it is the class imbalance that causes the poor detection of some classes of intrusion reported in the literature. An alternative approach to training ANNs is proposed in this thesis, using Genetic Algorithms (GAs) to evolve the weights of the ANNs, referred to as an Evolutionary Neural Network (ENN). When employing evaluation functions that calculate the fitness proportionally to the instances of each class, thereby avoiding a bias towards the major class(es) in the data set, significantly improved true positive rates are obtained whilst maintaining a low false positive rate. These findings demonstrate that the issues of learning from imbalanced data are not due to limitations of the ANNs; rather the training algorithm. Moreover, the ENN is capable of detecting a class of intrusion that has been reported in the literature to be undetectable by ANNs. One limitation of the ENN is a lack of control of the classification trade-off the ANNs obtain. This is identified as a general issue with current approaches to creating classifiers. Striving to create a single best classifier that obtains the highest accuracy may give an unfruitful classification trade-off, which is demonstrated clearly in this thesis. Therefore, an extension of the ENN is proposed, using a Multi-Objective GA (MOGA), which treats the classification rate on each class as a separate objective. This approach produces a Pareto front of non-dominated solutions that exhibit different classification trade-offs, from which the user can select one with the desired properties. The multi-objective approach is also utilised to evolve classifier ensembles, which yields an improved Pareto front of solutions. Furthermore, the selection of classifier members for the ensembles is investigated, demonstrating how this affects the performance of the resultant ensembles. This is a key to explaining why some classifier combinations fail to give fruitful solutions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Aeronautical Engineering. A continuing bibliography, supplement 112

This bibliography lists 424 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1979

Bluff Body Flow Simulation Using a Vortex Element Method

Heavy ground vehicles, especially those involved in long-haul freight transportation, consume a significant part of our nation's energy supply. it is therefore of utmost importance to improve their efficiency, both to reduce emissions and to decrease reliance on imported oil. At highway speeds, more than half of the power consumed by a typical semi truck goes into overcoming aerodynamic drag, a fraction which increases with speed and crosswind. Thanks to better tools and increased awareness, recent years have seen substantial aerodynamic improvements by the truck industry, such as tractor/trailer height matching, radiator area reduction, and swept fairings. However, there remains substantial room for improvement as understanding of turbulent fluid dynamics grows. The group's research effort focused on vortex particle methods, a novel approach for computational fluid dynamics (CFD). Where common CFD methods solve or model the Navier-Stokes equations on a grid which stretches from the truck surface outward, vortex particle methods solve the vorticity equation on a Lagrangian basis of smooth particles and do not require a grid. They worked to advance the state of the art in vortex particle methods, improving their ability to handle the complicated, high Reynolds number flow around heavy vehicles. Specific challenges that they have addressed include finding strategies to accurate capture vorticity generation and resultant forces at the truck wall, handling the aerodynamics of spinning bodies such as tires, application of the method to the GTS model, computation time reduction through improved integration methods, a closest point transform for particle method in complex geometrics, and work on large eddy simulation (LES) turbulence modeling

For the Voices : The Letters of John Wieners

American poet John Wieners is thoroughly disenfranchised from the modern poetic establishments because he is, to those institutions, practically illegible. He was a queer self-styled poete maudit in the fifties; a protege of political-historical poet Charles Olson who wrote audaciously personal verse; a lyric poet who eschewed the egoism of the confessional mode in order to pursue the Olsonian project of Projective (outward-looking) poetics; a Boston poet who was institutionalized at state hospitals. Wieners lived on the other side of Beacon Hill, not the Brahmin south slope, but the north side with its working-class apartments and underground gay bars. Though Wieners\u27 work is considered preeminent by many of the second half of the century\u27s most important poets, the ahistoricizing process of literary canon-building has kept him at the fringes of not just the canon, but the established taxonomy of the all the great post-war undergrounds - the mimeo revolution, the San Francisco Renaissance, Black Mountain, New York, and Boston poetry communities that he moved through. Why was Wieners so disenfranchised? How can we make him manifest within the discourses of twentieth-century poetry? My dissertation, a comprehensively edited and annotated Selected Letters with a critical introduction situating Wieners and his correspondence, will provide Wieners\u27 readers and literature scholars with an invaluable resource, an autobiography in letters. To quote the mission Duncan urged upon Wieners for his magazine Measure, these Selected Letters will be a ground of work for many different kinds of readers, with enough annotation and context for the most curious, but edited in such a way that it\u27s Wieners himself one is reading, a direct address with minimal editorial intrusion. Wieners dedicated his second book, 1964\u27s Ace of Pentacles, for the voices, and that is the title I take for this collection - for all the voices in Wieners\u27 world, within and contemporaneous with the poem. With these Selected Letters, we can see Wieners\u27 growth as a poet and as a person, as he cycles through his different selves and relationships