6,138 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
An intelligent alarm management system for large-scale telecommunication companies
This paper introduces an intelligent system that performs alarm correlation and root cause analysis. The system is designed to operate in large- scale heterogeneous networks from telecommunications operators. The pro- posed architecture includes a rules management module that is based in data mining (to generate the rules) and reinforcement learning (to improve rule se- lection) algorithms. In this work, we focus on the design and development of the rule generation part and test it using a large real-world dataset containing alarms from a Portuguese telecommunications company. The correlation engine achieved promising results, measured by a compression rate of 70% and as- sessed in real-time by experienced network administrator staff
SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis
In this paper, we propose a novel approach, called SENATUS, for joint traffic
anomaly detection and root-cause analysis. Inspired from the concept of a
senate, the key idea of the proposed approach is divided into three stages:
election, voting and decision. At the election stage, a small number of
\nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{,
which are used} to represent approximately the total (usually huge) set of
traffic flows. In the voting stage, anomaly detection is applied on the senator
flows and the detected anomalies are correlated to identify the most possible
anomalous time bins. Finally in the decision stage, a machine learning
technique is applied to the senator flows of each anomalous time bin to find
the root cause of the anomalies. We evaluate SENATUS using traffic traces
collected from the Pan European network, GEANT, and compare against another
approach which detects anomalies using lossless compression of traffic
histograms. We show the effectiveness of SENATUS in diagnosing anomaly types:
network scans and DoS/DDoS attacks
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