The Evolution of OSI Network Management by Integrated the Expert Knowledge

The management of modern telecommunications networks must satisfy ever-increasing operational demands. Operation and quality service requirements imposed by the users are also an important aspect to consider. In this paper we have carried out a study for the improvement of intelligent administration techniques in telecommunications networks. This task is achieved by integrating knowledge base of expert system within the management information used to manage a network. For this purpose, an extension of OSI management framework specifications language has been added and investigated in this study. A new property named RULE has also been added, which gathers important aspects of the facts and the knowledge base of the embedded expert system. Networks can be managed easily by using this proposed integration

Integrated Expert Management Knowledge on OSI Network Management Objects

The management of modern telecommunications networks must satisfy ever-increasing operational demands. We propose a study for the improvement of intelligent administration techniques in telecommunications networks. This task is achieved by integrating knowledge base of expert system within the management information used to manage a network. For this purpose, an extension of OSI management framework specifications language has been added and investigated. For this goal, we shall use the language Guidelines for the Definition of Managed Objects (GDMO) and a new property named RULE which gathers important aspects of the facts and the knowledge base of the embedded expert system. Networks can be managed easily by using this proposed integration

A selected glossary of electronic data interchange and related terms

School of Managemen

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

e-Process selection using decision making methods : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Palmerston North, New Zealand

The key objective of this research is to develop a selection methodology that can be used to support and aid the selection of development processes for e-Commerce Information Systems (eCIS) effectively using various decision methods. The selection methodology supports developers in their choice of an e-Commerce Information System Development Process (e-Process) by providing them with a few different decision making methods for choosing between defined e-Processes using a set of quality aspects to compare and evaluate the different options. The methodology also provides historical data of previous selections that can be used to further support their specific choice. The research was initiated by the fast growing Information Technology environment, where e-Commerce Information Systems is a relatively new development area and developers of these systems may be using new development methods and have difficulty deciding on the best suited process to use when developing new eCIS. These developers also need documentary support for their choices and this research helps them with these decision-making processes. The e-Process Selection Methodology allows for the comparison of existing development processes as well as the comparison of processes as defined by the developers. Four different decision making methods, the Value-Benefit Method (Weighted Scoring), the Analytical Hierarchy Process, Case-Based Reasoning and a Social Choice method are used to solve the problem of selecting among e-Commerce Development Methodologies. The Value-Benefit Method, when applied to the selection of an e-Process from a set of e-Processes, uses multiple quality aspects. Values are assigned to each aspect for each of the e-Processes by experts. The importance of each of the aspects, to the eCIS, is defined in terms of weights. The selected e-Process is the one with the highest score when the values and weights are multiplied and then summed. The Analytic Hierarchy Process is used to quantify a selection of quality aspects and then these are used to evaluate alternative e-Processes and thus determining the best matching solution to the problem. This process provides for the ranking and determining of the relative worth of each of the quality aspects. Case-Based Reasoning requires the capturing of the resulting knowledge of previous cases, in a knowledge base, in order to make a decision. The case database is built in such a way that the concrete factual knowledge of previous individual cases that were solved previously is stored and can be used in the decision process. Case-based reasoning is used to determine the best choices. This allows the user to either use the selection methodology or the case base database to resolve their problems or both. Social Choice Methods are based on voting processes. Individuals vote for their preferences from a set of e-Processes. The results are aggregated to obtain a final result that indicates which e-Process is the preferred one. The e-Process Selection Methodology is demonstrated and validated by the development of a prototype tool. This tool can be used to select the most suitable solution for a case at hand. The thesis includes the factors that motivated the research and the process that was followed. The e-Process Selection Methodology is summarised as well as the strengths and weaknesses discussed. The contribution to knowledge is explained and future developments are proposed. To conclude, the lessons learnt and reinforced are considered

An exploration of novel approaches to improve surveillance for infectious diseases in rural poultry of Zambia using Newcastle Disease as a case study

Poultry provides an important protein and revenue source for communities in tropical regions. Unfortunately, mechanisms for early detection of diseases in the rural poultry sector of developing countries like Zambia remain a challenge. Early detection of Newcastle Disease (ND) and other poultry diseases in domestic birds can reduce their spread. Understanding the status of priority poultry diseases like ND and movement of birds through trade will allow identification of disease and trade hotspots where frequent contact between birds can be expected and disease can be transmitted. Consequently, ensemble modelling was used to identify disease and trade hotspots with the aim of utilising them for rapid poultry disease detection. The approach involved a hazard and risk assessment which identified priority diseases and high disease risk hotspots for the rural poultry sector within Eastern Zambia respectively. This was followed by implementation and assessment of community based syndromic surveillance using poultry clubs (PCs). Newcastle Disease was identified as a priority disease. A retrospective study found that the disease followed a seasonal and cyclic pattern, with peaks in the hot dry season (Overall Seasonal Index 1.1) and had an estimated provincial incidence range of 0.16 to 1.7% per year, in eastern Zambia. Additionally, there were apparent spatial shifts in districts with outbreaks over time which could be because of veterinary interventions chasing outbreaks rather than implementing uniform control. When retrospective ND data was fitted to a predictive time series model, it showed an increasing trend in ND annual incidence over 25 years if existing interventions continue. The seroprevalence of ND among indigenous chickens that were not vaccinated against ND in Eastern Zambia was 73% (95% confidence interval 59-94%). Group specific reverse transcription assays and full genome sequencing identified NDV sub-genotypes VIIh and XIII, which were first identified in Asia, to be among the circulating ND viruses in Eastern Zambia. These findings revealed how vulnerable countries like Zambia are to exotic poultry disease infections. Descriptive and financial analysis of the rural poultry sector at the farm gate revealed that Poultry ranked highest in terms of popularity and numbers when compared with other animals kept by respondents (median=20). Gross margin analysis conducted using costing data from poultry farmers and expert opinion of extension workers revealed that indigenous chickens had the highest gross margin percentage (72%) compared to commercial broilers and layers which had gross margin percentages of 53% and 56% respectively. Breakeven analysis revealed that indigenous chickens required the lowest number of products to be sold (27) to realise profit compared to broilers (1011) and layers (873). The study further discusses how extension workers could utilise the weaknesses and strengths identified to initiate information sharing sessions with farmers that can arouse interest and ensure sustainable participation and implementation by farmers in sustainable disease extension programmes. A study that conducted social network analysis and analysed poultry trading practices revealed that some farmers and traders sourced their poultry from neighbouring countries thus justifying the need for regional collaboration when conducting poultry disease surveillance. Trade of poultry and its products was at its peak in December and January and was associated with Christmas and New Year celebrations respectively, thus providing information when surveillance should be taking place. This was the first study that formally described poultry movement networks within Zambia and the surrounding region. Its findings provided data required for implementing targeted surveillance in regions where resources are either inadequate or non-existent. A study that assessed the viability of syndromic data as a possible source for disease surveillance data found that farmers reported an overall annual disease incidence in rural poultry for eastern Zambia of 31% (90% CI 29-32%). On farm disease in poultry was associated with use of middlemen to purchase poultry products (p=0.05, OR=7.87), poultry products sold or given away from the farm (p=0.01, OR=1.92), farmers experiencing a period with more trade of poultry and its products (p=0.04, OR=1.70), presence of wild birds near the farm or village (p˂0.01, OR=2.47) and poultry diseases being reported from neighbouring farms or villages (p˂0.01, OR=3.12). The study also tentatively identified three poultry diseases (Newcastle Disease, Gumboro Disease and Fowl Pox) from the thirty-four disease syndromes provided by farmers. Farmers reported an incidence of 27% for Newcastle Disease in 2014. When compared with the state veterinary services data which reported Newcastle Disease incidence at 9% in 2014, it seems syndromic data obtained from farmers may be more sensitive in identifying disease incursion. The efficiency of PCs was assessed by computing the proportion of meetings conducted by PCs compared to the actual meetings planned. Sustainability was assessed by comparing the mean ranks of report submission of farmers over the 24 months post PC inception using the Friedman test. The effectiveness of disease surveillance using PCs was evaluated by determining the minimum number of reports required from club members to detect at least one household with poultry disease in the population. This was modelled further using a geometric distribution function to establish the sensitivity of the reporting system. Additionally, PCs were evaluated using focussed group discussions and structured questionnaire interviews. The syndrome reporting efficiency of PCs was 0.8. The PC approach was sustainable because there were no significant differences in report submission between the 24 months post inception (Friedman test, χ2(23) = 32.93, p = 0.08). The probability of detecting outbreaks in disease hotspots of Eastern Zambia was estimated at 98% (51-100). Most respondents were either very satisfied or extremely satisfied with the approach. The study concluded that PCs can be used as a community-based platform for low cost syndromic surveillance that is sustainable. Using ensemble modelling, the project managed to set up a viable system for rapid detection of poultry diseases which utilised disease and trade hotspots among the rural poultry sector in Eastern Zambia. Through its studies this research revealed key disease control issues which could be extrapolated to other regions and its model may be applied to enhance disease surveillance for other livestock such as pigs, goats, cattle and aquaculture