6,780 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
A synthesis of logic and bio-inspired techniques in the design of dependable systems
Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules
The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques
The paper describes VEX-93 as a hybrid environment for developing
knowledge-based and problem solver systems. It integrates methods and
techniques from artificial intelligence, image and signal processing and
data analysis, which can be mixed. Two hierarchical levels of reasoning
contains an intelligent toolbox with one upper strategic inference engine
and four lower ones containing specific reasoning models: truth-functional
(rule-based), probabilistic (causal networks), fuzzy (rule-based) and
case-based (frames). There are image/signal processing-analysis capabilities
in the form of programming languages with more than one hundred primitive
functions.
User-made programs are embeddable within knowledge basis, allowing the
combination of perception and reasoning. The data analyzer toolbox contains
a collection of numerical classification, pattern recognition and ordination
methods, with neural network tools and a data base query language at
inference engines's disposal.
VEX-93 is an open system able to communicate with external computer programs
relevant to a particular application. Metaknowledge can be used for
elaborate conclusions, and man-machine interaction includes, besides windows
and graphical interfaces, acceptance of voice commands and production of
speech output.
The system was conceived for real-world applications in general domains, but
an example of a concrete medical diagnostic support system at present under
completion as a cuban-spanish project is mentioned.
Present version of VEX-93 is a huge system composed by about one and half
millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version
Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems
Abstract— Nowadays careful measurement applications are
handed over to Wired and Wireless Sensor Network. Taking
the scenario of train location as an example, this would lead to
an increase in uncertainty about position related to sensors
with long acquisition times like Balises, RFID and
Transponders along the track. We take into account the data
without any synchronization protocols, for increase the
accuracy and reduce the uncertainty after the data fusion
algorithms. The case studies, we have analysed, derived from
the needs of the project partners: train localization, head of an
auger in the drilling sector localization and the location of
containers of radioactive material waste in a reprocessing
nuclear plant. They have the necessity to plan the maintenance
operations of their infrastructure basing through architecture
that taking input from the sensors, which are localization and
diagnosis, maps and cost, to optimize the cost effectiveness and
reduce the time of operation
Probabilistic and fuzzy reasoning in simple learning classifier systems
This paper is concerned with the general stimulus-response problem as addressed by a variety of simple learning c1assifier systems (CSs). We suggest a theoretical model from which the assessment of uncertainty emerges as primary concern. A number of representation schemes borrowing from fuzzy logic theory are reviewed, and sorne connections with a well-known neural architecture revisited. In pursuit of the uncertainty measuring goal, usage of explicit probability distributions in the action part of c1assifiers is advocated. Sorne ideas supporting the design of a hybrid system incorpo'rating bayesian learning on top of the CS basic algorithm are sketched
The safety case and the lessons learned for the reliability and maintainability case
This paper examine the safety case and the lessons learned for the reliability and maintainability case
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