29,963 research outputs found
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
Self-tuning routine alarm analysis of vibration signals in steam turbine generators
This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques
Cloaking the Clock: Emulating Clock Skew in Controller Area Networks
Automobiles are equipped with Electronic Control Units (ECU) that communicate
via in-vehicle network protocol standards such as Controller Area Network
(CAN). These protocols are designed under the assumption that separating
in-vehicle communications from external networks is sufficient for protection
against cyber attacks. This assumption, however, has been shown to be invalid
by recent attacks in which adversaries were able to infiltrate the in-vehicle
network. Motivated by these attacks, intrusion detection systems (IDSs) have
been proposed for in-vehicle networks that attempt to detect attacks by making
use of device fingerprinting using properties such as clock skew of an ECU. In
this paper, we propose the cloaking attack, an intelligent masquerade attack in
which an adversary modifies the timing of transmitted messages in order to
match the clock skew of a targeted ECU. The attack leverages the fact that,
while the clock skew is a physical property of each ECU that cannot be changed
by the adversary, the estimation of the clock skew by other ECUs is based on
network traffic, which, being a cyber component only, can be modified by an
adversary. We implement the proposed cloaking attack and test it on two IDSs,
namely, the current state-of-the-art IDS and a new IDS that we develop based on
the widely-used Network Time Protocol (NTP). We implement the cloaking attack
on two hardware testbeds, a prototype and a real connected vehicle, and show
that it can always deceive both IDSs. We also introduce a new metric called the
Maximum Slackness Index to quantify the effectiveness of the cloaking attack
even when the adversary is unable to precisely match the clock skew of the
targeted ECU.Comment: 11 pages, 13 figures, This work has been accepted to the 9th ACM/IEEE
International Conference on Cyber-Physical Systems (ICCPS
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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