A framework for modelling mobile radio access networks for intelligent fault management

Postprin

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

[[alternative]]Computer-Aided Tactics Support in Negotiation

計畫編號：NSC92-2213-E032-028研究期間：200308~200407研究經費：414,000[[sponsorship]]行政院國家科學委員

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

FILTERING FALSE ALARMS: AN APPROACH BASED ON EPISODE MINING

The security of computer networks is a prime concern today. Various devices and methods have been developed to offer different kinds of protection (firewalls, IDS´s, antiviruses, etc.). By centrally storing and processing the signals of these devices, it is possible to detect more cheats and attacks than simply by analysing the logs independently. The most difficult and still unsolved problem in centralized systems is that vast numbers of false alarms. If a harmless pattern, which caused by a safe operation is identified as an alarm, then it is a nuisance and requires human invention to be handled properly. In this paper we show how we can use data mining to discover the patterns that frequently causes false alarms. Due to the new requirements (events with many attributes, invertible parametric predicates) none of the previously published algorithms can be applied to our problem directly. We present the algorithm ABAMSEP, which discovers frequent alert-ended episodes. We prove that the algorithm is correct in the sense that it finds all episodes that meet the requirements of the specification