20,918 research outputs found
Cost-Bounded Active Classification Using Partially Observable Markov Decision Processes
Active classification, i.e., the sequential decision-making process aimed at
data acquisition for classification purposes, arises naturally in many
applications, including medical diagnosis, intrusion detection, and object
tracking. In this work, we study the problem of actively classifying dynamical
systems with a finite set of Markov decision process (MDP) models. We are
interested in finding strategies that actively interact with the dynamical
system, and observe its reactions so that the true model is determined
efficiently with high confidence. To this end, we present a decision-theoretic
framework based on partially observable Markov decision processes (POMDPs). The
proposed framework relies on assigning a classification belief (a probability
distribution) to each candidate MDP model. Given an initial belief, some
misclassification probabilities, a cost bound, and a finite time horizon, we
design POMDP strategies leading to classification decisions. We present two
different approaches to find such strategies. The first approach computes the
optimal strategy "exactly" using value iteration. To overcome the computational
complexity of finding exact solutions, the second approach is based on adaptive
sampling to approximate the optimal probability of reaching a classification
decision. We illustrate the proposed methodology using two examples from
medical diagnosis and intruder detection
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
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Detection of Security and Dependability Threats: A Belief Based Reasoning Approach
Monitoring the preservation of security and dependability (S&D) properties during the operation of systems at runtime is an important verification measure that can increase system resilience. However it does not always provide sufficient scope for taking control actions against violations as it only detects problems after they occur. In this paper, we describe a proactive monitoring approach that detects potential violations of S&D properties, called ldquothreatsrdquo, and discuss the results of an initial evaluation of it
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