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

The Structural Crisis of Labor Flexibility

Paper evaluating the CCC’s aims, strategies, and activities. It includes an analysis of the persistence of poor working conditions in the garment industry; an overview of CCC strategies and the debate over codes of conduct, monitoring, and verification; and the description of three broad strategies for future action aimed at increasing the impact of voluntary, private instruments on working conditions

Multiple object tracking using a neural cost function

This paper presents a new approach to the tracking of multiple objects in CCTV surveillance using a combination of simple neural cost functions based on Self-Organizing Maps, and a greedy assignment algorithm. Using a reference standard data set and an exhaustive search algorithm for benchmarking, we show that the cost function plays the most significant role in realizing high levels of performance. The neural cost function’s context-sensitive treatment of appearance, change of appearance and trajectory yield better tracking than a simple, explicitly designed cost function. The algorithm matches 98.8% of objects to within 15 pixels

Complex systems science: expert consultation report

Executive SummaryA new programme of research in Complex Systems Science must be initiated by FETThe science of complex systems (CS) is essential to establish rigorous scientific principles on which to develop the future ICT systems that are critical to the well-being, safety and prosperity of Europe and its citizens. As the “ICT incubator and pathfinder for new ideas and themes for long-term research in the area of information and communication technologies” FET must initiate a significant new programme of research in complex systems science to underpin research and development in ICT. Complex Systems Science is a “blue sky” research laboratory for R&D in ICT and their applications. In July 2009, ASSYST was given a set of probing questions concerning FET funding for ICT-related complex systems research. This document is based on the CS community’s response.Complex systems research has made considerable progress and is delivering new scienceSince FET began supporting CS research, considerable progress has been made. Building on previous understanding of concepts such as emergence from interactions, far-from-equilibrium systems, border of chaos and self-organised criticality, recent CS research is now delivering rigorous theory through methods of statistical physics, network theory, and computer simulation. CS research increasingly demands high-throughput data streams and new ICT-based methods of observing and reconstructing, i.e. modelling, the dynamics from those data in areas as diverse as embryogenesis, neuroscience, transport, epidemics, linguistics, meteorology, and robotics. CS research is also beginning to address the problem of engineering robust systems of systems of systems that can adapt to changing environments, including the perplexing problem that ICT systems are too often fragile and non-adaptive.Recommendation: A Programme of Research in Complex Systems Science to Support ICTFundamental theory in Complex Systems Science is needed, but this can only be achieved through real-world applications involving large, heterogeneous, and messy data sets, including people and organisations. A long-term vision is needed. Realistic targets can be set. Fundamental research can be ensured by requiring that teams include mathematicians, computer scientists, physicists and computational social scientists.One research priority is to develop a formalism for multilevel systems of systems of systems, applicable to all areas including biology, economics, security, transportation, robotics, health, agriculture, ecology, and climate change. Another related research priority is a scientific perspective on the integration of the new science with policy and its implementation, including ethical problems related to privacy and equality.A further priority is the need for education in complex systems science. Conventional education continues to be domain-dominated, producing scientists who are for the most part still lacking fundamental knowledge in core areas of mathematics, computation, statistical physics, and social systems. Therefore:1. We recommend that FET fund a new programme of work in complex systems science as essential research for progress in the development of new kinds of ICT systems.2. We have identified the dynamics of multilevel systems as the area in complex systems science requiring a major paradigm shift, beyond which significant scientific progress cannot be made.3. We propose a call requiring: fundamental research in complex systems science; new mathematical and computational formalisms to be developed; involving a large ‘guinea pig’ organisation; research into policy and its meta-level information dynamics; and that all research staff have interdisciplinary knowledge through an education programme.Tangible outcomes, potential users of the new science, its impact and measures of successUsers include (i) the private and public sectors using ICT to manage complex systems and (ii) researchers in ICT, CSS, and all complex domains. The tangible output of a call will be new knowledge on the nature of complex systems in general, new knowledge of the particular complex system(s) studied, and new knowledge of the fundamental role played by ICT in the research and implementation to create real systems addressing real-world problems. The impact of the call will be seen through new high added-value opportunities in the public and private sectors, new high added-value ICT technologies, and new high added-value science to support innovation in ICT research and development. The measure of success will be through the delivery of these high added-value outcomes, and new science to better understand failures

The development of artificial neural networks for the analysis of market research and electronic nose data

This thesis details research carried out into the application of unsupervised neural network and statistical clustering techniques to market research interview survey analysis. The objective of the research was to develop mathematical mechanisms to locate and quantify internal clusters within the data sets with definite commonality. As the data sets being used were binary, this commonality was expressed in terms of identical question answers. Unsupervised neural network paradigms are investigated, along with statistical clustering techniques. The theory of clustering in a binary space is also looked at. Attempts to improve the clarity of output of Self-Organising Maps (SOM) consisted of several stages of investigation culminating in the conception of the Interrogative Memory Structure (lMS). IMS proved easy to use, fast in operation and consistently produced results with the highest degree of commonality when tested against SOM, Adaptive Resonance Theory (ART!) and FASTCLUS. ARTl performed well when clusters were measured using general metrics. During the course of the research a supervised technique, the Vector Memory Array (VMA), was developed. VMA was tested against Back Propagation (BP) (using data sets provided by the Warwick electronic nose project) and consistently produced higher classification accuracies. The main advantage of VMA is its speed of operation - in testing it produced results in minutes compared to hours for the BP method, giving speed increases in the region of 100: 1

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Exploring emergence in corporate sustainability

As the impacts of climate change intensify, businesses are increasingly committing to ambitious sustainable development goals, yet an enduring disconnect remains between corporate sustainability activities and declining global environment and society. This study adopts a complexity view that reductionism associated with Newtonian thinking has played a key role in creating many of the sustainability issues now faced by humanity. This dissertation departs from the premise that sustainability needs to be integrated into an organisation and uses a complexity view to argue that corporate sustainability is a co-evolutionary process of emergence. Whilst many studies have examined how sustainability can be integrated into a business, less is known about corporate sustainability as an emergent process. To address the knowledge gap, this research answered three questions: (1) How does sustainability emerge in financial institutions? (2) What is the role of coherence in the emergence of sustainability? and (3) What conditions enable the emergence of sustainability? A mixed method sequential design was used. In the initial quantitative strand of the research, a holistic business assessment survey based on integral theory was implemented in two financial services organisations in Southern Africa. The results were analysed using self-organising maps and explored in narrative interviews in the subsequent qualitative strand of the research. The study makes three contributions to our understanding of emergence in corporate sustainability. First, by proposing four modes by which corporate sustainability is enacted; these elucidate how integral domains are enacted in corporate sustainability. Second, by clarifying the process of emergence by articulating how zones of coherence emerge between embodied and embedded dimensions. Third, by explaining how the shift to corporate sustainability occurs by means of four conditions. These contributions serve to advance our understanding of corporate sustainability as a fundamental shift in the functioning of an organisation towards coevolutionary self-organisation. It is recommended that corporate sustainability is holistically cultivated to support emergence and self-organisation, rather than being integrated through a linear process of change