53,932 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
Integrative Genomics Reveals the Genetics and Evolution of the Honey Bee’s Social Immune System
Social organisms combat pathogens through individual innate immune responses or through social immunity—behaviors among individuals that limit pathogen transmission within groups. Although we have a relatively detailed understanding of the genetics and evolution of the innate immune system of animals, we know little about social immunity. Addressing this knowledge gap is crucial for understanding how life-history traits influence immunity, and identifying if trade-offs exist between innate and social immunity. Hygienic behavior in the Western honey bee, Apis mellifera, provides an excellent model for investigating the genetics and evolution of social immunity in animals. This heritable, colony-level behavior is performed by nurse bees when they detect and remove infected or dead brood from the colony. We sequenced 125 haploid genomes from two artificially selected highly hygienic populations and a baseline unselected population. Genomic contrasts allowed us to identify a minimum of 73 genes tentatively associated with hygienic behavior. Many genes were within previously discovered QTLs associated with hygienic behavior and were predictive of hygienic behavior within the unselected population. These genes were often involved in neuronal development and sensory perception in solitary insects. We found that genes associated with hygienic behavior have evidence of positive selection within honey bees (Apis), supporting the hypothesis that social immunity contributes to fitness. Our results indicate that genes influencing developmental neurobiology and behavior in solitary insects may have been co-opted to give rise to a novel and adaptive social immune phenotype in honey bees.York University Librarie
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