Integration of cost-risk assessment of denial of service within an intelligent maintenance system

As organisations become richer in data the function of asset management will have to increasingly use intelligent systems to control condition monitoring systems and organise maintenance. In the future the UK rail industry is anticipating having to optimize capacity by running trains closer to each other. In this situation maintenance becomes extremely problematic as within such a high-performance network a relatively minor fault will impact more trains and passengers; such denial of service causes reputational damage for the industry and causes fines to be levied against the infrastructure owner, Network Rail. Intelligent systems used to control condition monitoring systems will need to optimize for several factors; optimization for minimizing denial of service will be one such factor. With schedules anticipated to be increasingly complicated detailed estimation methods will be extremely difficult to implement. Cost prediction of maintenance activities tend to be expert driven and require extensive details, making automation of such an activity difficult. Therefore a stochastic process will be needed to approach the problem of predicting the denial of service arising from any required maintenance. Good uncertainty modelling will help to increase the confidence of estimates. This paper seeks to detail the challenges that the UK Railway industry face with regards to cost modelling of maintenance activities and outline an example of a suitable cost model for quantifying cost uncertainty. The proposed uncertainty quantification is based on historical cost data and interpretation of its statistical distributions. These estimates are then integrated in a cost model to obtain accurate uncertainty measurements of outputs through Monte-Carlo simulation methods. An additional criteria of the model was that it be suitable for integration into an existing prototype integrated intelligent maintenance system. It is anticipated that applying an integrated maintenance management system will apply significant downward pressure on maintenance budgets and reduce denial of service. Accurate cost estimation is therefore of great importance if anticipated cost efficiencies are to be achieved. While the rail industry has been the focus of this work, other industries have been considered and it is anticipated that the approach will be applicable to many other organisations across several asset management intensive industrie

Kyberuhat konttisataman automaatiojärjestelmässä

The rapid development in connectivity of Industrial Control Systems has created a new security threat in all industrial sectors, and the maritime sector is no exception. Therefore this thesis explores cyber threats in a container terminal automation system using two methods: literature review and attack tree analysis. In this thesis, cyber threats in Industrial Control Systems were first studied in general by the means of a literature review. Then, the identified threats were applied to a software component of a terminal automation system using attack trees. Attack trees are a tool that helps in visualizing different cyber attacks. Based on the results, threats were classified in risk categories and the most problematic areas were identified. Finally, suggestions were made on how to improve cyber security of the component assessed and of the terminal automation system in general. Based on the literature review, ten different risk categories were identified. The categories cover various attacks ranging from malware and Denial-of-Service attacks all the way to physical and social attacks. When assessing the software component, three problem areas were identified: susceptibility to Denial-of-Service attacks, weak protection of communication and vulnerability of a certain software sub-component. The suggested security improvements include changes to the network design, use of stronger authentication and better management of the process automation network

Kyberuhat konttisataman automaatiojärjestelmässä

The rapid development in connectivity of Industrial Control Systems has created a new security threat in all industrial sectors, and the maritime sector is no exception. Therefore this thesis explores cyber threats in a container terminal automation system using two methods: literature review and attack tree analysis. In this thesis, cyber threats in Industrial Control Systems were first studied in general by the means of a literature review. Then, the identified threats were applied to a software component of a terminal automation system using attack trees. Attack trees are a tool that helps in visualizing different cyber attacks. Based on the results, threats were classified in risk categories and the most problematic areas were identified. Finally, suggestions were made on how to improve cyber security of the component assessed and of the terminal automation system in general. Based on the literature review, ten different risk categories were identified. The categories cover various attacks ranging from malware and Denial-of-Service attacks all the way to physical and social attacks. When assessing the software component, three problem areas were identified: susceptibility to Denial-of-Service attacks, weak protection of communication and vulnerability of a certain software sub-component. The suggested security improvements include changes to the network design, use of stronger authentication and better management of the process automation network

Shielding against Web Application Attacks - Detection Techniques and Classification

The field of IoT web applications is facing a range of security risks and system attacks due to the increasing complexity and size of home automation datasets. One of the primary concerns is the identification of Distributed Denial of Service (DDoS) attacks in home automation systems. Attackers can easily access various IoT web application assets by entering a home automation dataset or clicking a link, making them vulnerable to different types of web attacks. To address these challenges, the cloud has introduced the Edge of Things paradigm, which uses multiple concurrent deep models to enhance system stability and enable easy data revelation updates. Therefore, identifying malicious attacks is crucial for improving the reliability and security of IoT web applications. This paper uses a Machine Learning algorithm that can accurately identify web attacks using unique keywords. Smart home devices are classified into four classes based on their traffic predictability levels, and a neural system recognition model is proposed to classify these attacks with a high degree of accuracy, outperforming other classification models. The application of deep learning in identifying and classifying attacks has significant theoretical and scientific value for web security investigations. It also provides innovative ideas for intelligent security detection by classifying web visitors, making it possible to identify and prevent potential security threats

A Survey on Communication Networks for Electric System Automation

Published in Computer Networks 50 (2006) 877–897, an Elsevier journal. The definitive version of this publication is available from Science Direct. Digital Object Identifier:10.1016/j.comnet.2006.01.005In today’s competitive electric utility marketplace, reliable and real-time information become the key factor for reliable delivery of power to the end-users, profitability of the electric utility and customer satisfaction. The operational and commercial demands of electric utilities require a high-performance data communication network that supports both existing functionalities and future operational requirements. In this respect, since such a communication network constitutes the core of the electric system automation applications, the design of a cost-effective and reliable network architecture is crucial. In this paper, the opportunities and challenges of a hybrid network architecture are discussed for electric system automation. More specifically, Internet based Virtual Private Networks, power line communications, satellite communications and wireless communications (wireless sensor networks, WiMAX and wireless mesh networks) are described in detail. The motivation of this paper is to provide a better understanding of the hybrid network architecture that can provide heterogeneous electric system automation application requirements. In this regard, our aim is to present a structured framework for electric utilities who plan to utilize new communication technologies for automation and hence, to make the decision making process more effective and direct.This work was supported by NEETRAC under Project #04-157