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A Survey of Distributed Intrusion Detection Approaches
Distributed intrustion detection systems detect attacks on computer systems
by analyzing data aggregated from distributed sources. The distributed nature
of the data sources allows patterns in the data to be seen that might not be
detectable if each of the sources were examined individually. This paper
describes the various approaches that have been developed to share and analyze
data in such systems, and discusses some issues that must be addressed before
fully decentralized distributed intrusion detection systems can be made viable
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A survey of intrusion detection techniques in Cloud
Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks
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 methodology for the quantitative evaluation of attacks and mitigations in IoT systems
PhD ThesisAs we move towards a more distributed and unsupervised internet, namely through the
Internet of Things (IoT), the avenues of attack multiply. To compound these issues, whilst
attacks are developing, the current security of devices is much lower than for traditional
systems.
In this thesis I propose a new methodology for white box behaviour intrusion detection
in constrained systems. I leverage the characteristics of these types of systems, namely their:
heterogeneity, distributed nature, and constrained capabilities; to devise a pipeline, that given
a specification of a IoT scenario can generate an actionable intrusion detection system to
protect it.
I identify key IoT scenarios for which more traditional black box approaches would
not suffice, and devise means to bypass these limitations. The contributions include; 1) A
survey of intrusion detection for IoT; 2) A modelling technique to observe interactions in IoT
deployments; 3) A modelling approach that focuses on the observation of specific attacks
on possible configurations of IoT devices; Combining these components: a specification
of the system as per contribution 1 and a attack specification as per contribution 2, we can
deploy a bespoke behaviour based IDS for the specified system. This one of a kind approach
allows for the quick and efficient generation of attack detection from the onset, positioning
this approach as particularly suitable to dynamic and constrained IoT environments
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