RISK ANALYSIS AND MANAGEMENT OF SECURITY THREATS IN VIRTUALISED INFORMATION SYSTEMS USING PREDICTIVE ANALYTICS

The use of online server applications has increased in recent years. To achieve the benefits of these technologies, cloud computing, with its ability to use virtual machine technologies to overcome limitations and guarantee security and quality of service to its end user customer, is being used as a platform to run online server applications. This however brings about a number of security issues aimed specifically at virtual machine technologies. A number of security solutions like virtual machine introspection, intrusion detection and many more, have been proposed and implemented, but the question to combat security issues in near or even real time still remains. To help answer the above question or even move a step further from the existing solutions, which still use data mining techniques to combat the security issues of virtualisation, we propose the novel use of predictive analytics for risk analysis and management of security threats in virtualised information systems as well as design and implement a novel predictive analytics framework used to design build and implement the same predictive analytics model In this project, we adopt the use of predictive analytics and demonstrate how it can be used for managing risks and security of virtualised environments. An experimental testbed for the simulation of attacks and data collection is set-up. Exploratory data analytics process is carried out to prepare the data for predictive modelling. A linear regression predictive model is built using the results from the exploratory data analytics using linear regression algorithm. The model is then validated and tested for predictive accuracy using Naïve Bayes and logistic algorithms respectively. Time series algorithms are then used to build a time series predictive model that will predict attacks (DoS attacks in this case) in real time using new data. Designing and implementing the proposed predictive analytics model, which is aimed at monitoring, analysing and mitigating security threats in real time successfully demonstrates the use of predictive analytics modelling as a security management tool for virtualised information systems as a novel contribution to virtualisation security

Water demand forecasting for the optimal operation of large-scale drinking water networks: The Barcelona case study

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica).Drinking Water Networks (DWN) are large-scale multiple-input multiple-output systems with uncertain disturbances (such as the water demand from the consumers) and involve components of linear, non-linear and switching nature. Operating, safety and quality constraints deem it important for the state and the input of such systems to be constrained into a given domain. Moreover, DWNs' operation is driven by time-varying demands and involves an considerable consumption of electric energy and the exploitation of limited water resources. Hence, the management of these networks must be carried out optimally with respect to the use of available resources and infrastructure, whilst satisfying high service levels for the drinking water supply. To accomplish this task, this paper explores various methods for demand forecasting, such as Seasonal ARIMA, BATS and Support Vector Machine, and presents a set of statistically validated time series models. These models, integrated with a Model Predictive Control (MPC) strategy addressed in this paper, allow to account for an accurate on-line forecasting and flow management of a DWN.This work was financially supported by the EU FP7 research project EFFINET “Efficient Integrated Real-time monitoring and Control of Drinking Water Networks,” grant agreement no. 318556.Peer Reviewe

Integrating real-time simulation models into a SCADA environment : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology at Massey University

Control system engineers have always envisaged the prospect of using the real-time models in an industrial setting. The inclusion of the real-time models can benefit industry in the following ways. 1. Operator Training - The operator can learn about how the various process react to control actions with the help of simulation models without affecting the real process itself. 2. Control Systems testing - The simulation models can be helpful in testing the control system software prior to trialing it on the real process. 3. Proccss Monitoring - Operators can compare the real process outputs with the simulation model outputs. This helps them in stopping the process when unusual conditions occur. 4. Testing for optimum operating conditions - Simulation models can be used to test for optimum operating conditions or for testing a certain operation at a new operating condition without affecting the real process. 5. Implementation of advanced control strategies - Advanced control strategics such as multivariable control, model predictive control and non linear control can be implemented as a real-time model without the development of separate real-time software. Even though using the real-time models can benefit the industry as mentioned modeling and real-time models have not found much favour in the industry. The reasons for this may be as follows: 1. Lack of awareness - Most of the plant managers/operators fail to understand what modeling results in and how it can improve the overall plant operation. 2. Lack of expertise - There is no expertise and/or tools in the company to develop the simulation models and implement it. 3. Cost of modeling - Producing a simulation model incurs significant costs. 4. Cost of implementation - Once the model is developed in the development environment it has to be transferred to the industrial platform. The cost of this transfer is high as the model software has to be more robust than the general purpose software. In order to produce real-time simulation models for an industrial setting there are two significant environments required. These are the development environment where the model is developed and secondly the implementation environment, where the model is used

An advanced real-time predictive maintenance framework for large scale machine systems

This thesis introduces and develops a novel real-time predictive maintenance system to estimate the machine system parameters using the motion current signature. Recently, motion current signature analysis has been addressed as an alternative to the use of sensors for monitoring internal faults of a motor. A maintenance system based upon the analysis of motion current signature avoids the need for the implementation and maintenance of expensive motion sensing technology. By developing nonlinear dynamical analysis for motion current signature, the research described in this thesis implements a novel real-time predictive maintenance system for current and future manufacturing machine systems. A crucial concept underpinning this project is that the motion current signature contains infor­mation relating to the machine system parameters and that this information can be extracted using nonlinear mapping techniques, such as neural networks. Towards this end, a proof of con­cept procedure is performed, which substantiates this concept. A simulation model, TuneLearn, is developed to simulate the large amount of training data required by the neural network ap­proach. Statistical validation and verification of the model is performed to ascertain confidence in the simulated motion current signature. Validation experiment concludes that, although, the simulation model generates a good macro-dynamical mapping of the motion current signature, it fails to accurately map the micro-dynamical structure due to the lack of knowledge regarding performance of higher order and nonlinear factors, such as backlash and compliance. Failure of the simulation model to determine the micro-dynamical structure suggests the pres­ence of nonlinearity in the motion current signature. This motivated us to perform surrogate data testing for nonlinearity in the motion current signature. Results confirm the presence of nonlinearity in the motion current signature, thereby, motivating the use of nonlinear tech­niques for further analysis. Outcomes of the experiment show that nonlinear noise reduction combined with the linear reverse algorithm offers precise machine system parameter estimation using the motion current signature for the implementation of the real-time predictive maintenance system. Finally, a linear reverse algorithm, BJEST, is developed and applied to the motion current signature to estimate the machine system parameters

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad