12,903 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
Application of an AIS to the problem of through life health management of remotely piloted aircraft
The operation of RPAS includes a cognitive problem for the operators(Pilots,
maintainers, ,managers, and the wider organization) to effectively maintain their situational
awareness of the aircraft and predict its health state. This has a large impact on their ability
to successfully identify faults and manage systems during operations. To overcome these
system deficiencies an asset health management system that integrates more cognitive
abilities to aid situational awareness could prove beneficial. This paper outlines an artificial
immune system (AIS) approach that could meet these challenges and an experimental
method within which to evaluate it
Artificial immune systems
The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self or nonself substances. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years
An Immune Inspired Approach to Anomaly Detection
The immune system provides a rich metaphor for computer security: anomaly
detection that works in nature should work for machines. However, early
artificial immune system approaches for computer security had only limited
success. Arguably, this was due to these artificial systems being based on too
simplistic a view of the immune system. We present here a second generation
artificial immune system for process anomaly detection. It improves on earlier
systems by having different artificial cell types that process information.
Following detailed information about how to build such second generation
systems, we find that communication between cells types is key to performance.
Through realistic testing and validation we show that second generation
artificial immune systems are capable of anomaly detection beyond generic
system policies. The paper concludes with a discussion and outline of the next
steps in this exciting area of computer security.Comment: 19 pages, 4 tables, 2 figures, Handbook of Research on Information
Security and Assuranc
Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm
Dendritic cells are antigen presenting cells that provide a vital link
between the innate and adaptive immune system, providing the initial detection
of pathogenic invaders. Research into this family of cells has revealed that
they perform information fusion which directs immune responses. We have derived
a Dendritic Cell Algorithm based on the functionality of these cells, by
modelling the biological signals and differentiation pathways to build a
control mechanism for an artificial immune system. We present algorithmic
details in addition to experimental results, when the algorithm was applied to
anomaly detection for the detection of port scans. The results show the
Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio
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