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

Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

A new continuous learning method is used to optimise the selection of services in response to user requests in an active computer network simulation environment. The learning is an enhanced version of the ‘snap-drift’ algorithm, which employs the complementary concepts of fast, minimalist (snap) learning and slower drift (towards the input patterns) learning, in a non-stationary environment where new patterns arrive continually. Snap is based on Adaptive Resonance Theory, and drift on Learning Vector Quantisation. The new algorithm swaps its learning style between these two self-organisational modes when declining performance is detected, but maintains the same learning mode during episodes of improved performance. Performance updates occur at the end of each epoch. Reinforcement is implemented by enabling learning on any given pattern with a probability that increases linearly with declining performance. This method, which is capable of rapid re-learning, is used in the design of a modular neural network system: Performance-guided Adaptive Resonance Theory (P-ART). Simulations involving a requirement to continuously adapt to make appropirate decisions within a BT active computer network environment, demonstrate the learning is stable, and able to discover alternative solutions in rapid response to new performance requirements or significant changes in the stream of input patterns

A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Reinforcement learning in intelligent control : a biologically-inspired approach to the relearning problem

Merged with duplicate record 10026.1/2240 on 08.20.2017 by CS (TIS)The increasingly complex demands placed on control systems have resulted in a need for intelligent control, an approach that attempts to meet these demands by emulating the capabilities found in biological systems. The need to exploit existing knowledge is a desirable feature of any intelligent control system, and this leads to the relearning problem. The problem arises when a control system is required to effectively learn new knowledge whilst exploiting still useful knowledge from past experiences. This thesis describes the adaptive critic system using reinforcement learning, a computational framework that can effectively address many of the demands in intelligent control, but is less effective when it comes to addressing the relearning problem. The thesis argues that biological mechanisms of reinforcement learning (and relearning) may provide inspiration for developing artificial intelligent control mechanisms that can better address the relearning problem. A conceptual model of biological reinforcement learning and relearning is presented, and the thesis shows how inspiration derived from this model can be used to modify the adaptive critic. The performance of the modified adaptive critic system on the relearning problem is investigated based on simulations of the pole balancing problem, and this is compared to the performance of the original adaptive critic system. The thesis presents an analysis of the results from these simulations, and discusses the significance of these results in terms of addressing the relearning problem

Object-oriented analysis and design of computational intelligence systems

Machine learning from data, neuro-fuzzy information processing, approximate reasoning and genetic and evolutionary computation are all aspects of computational intelligence (also called soft computing methods). Soft computing methods differ from conventional computing in that they are tolerant of imprecision, uncertainty and partial truths. These characteristics can be exploited to achieve tractability, robustness and low solution costs when the solution to a complex (in machine terms) problem is required. The principal constituents of soft computing include: Neural Networks, Fuzzy Logic and Probabilistic Reasoning Systems. Genetic Algorithms (GAs), Evolutionary Algorithms, Chaos Theory', Complexity Theory and parts of Learning Theory all come under Probabilistic Reasoning Systems. Hybrid systems can be designed incorporating 2 or more aspects of soft computing that are more powerful than any of the components used in a stand alone fashion. A unified framework is needed to implement and manipulate such systems. Such a framework will allow for easy visualisation of the underlying concepts and easy modification of the resulting computer models. In this thesis, an investigation of the major aspects of computational intelligence has been carried out. The main emphasis has been placed on developing an object-oriented framework for architecting computational intelligence systems. Object models for Neural Networks, Fuzzy Logic Systems and Evolutionary Computation systems have been developed. Software has been written in C++ to realise sample implementations of the various systems. Finally, practical applications and the results of using the Neural Networks, Fuzzy Logic systems and Genetic Algorithms developed in solving real world problems are presented. A consistent notation based on the Object Modelling Technique (OMT) is used throughout the thesis to describe the software architectures from which the computer implementation models have been derived

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Artificial neural networks for the classification of Meliaceae extractives.

Thesis (Ph.D.)-University of Natal, Durban, 1998.The goal of this project was the development of a computer-based system using artificial intelligence to classify the limonoids, protolimonoids and triterpenoids isolated from the family Meliaceae by the Natural Products Research Group of the University of Natal, Durban. A database of samples was obtained between 1991 and 1996, part of which time the author was a member of the group and isolated compounds from Turraea obtusifolia and Turraea floribunda. Over and above the problem of complexity and similarity in structures of the above mentioned natural products, are other difficulties. These include very small amounts of sample being isolated producing very weak peak signals in the C-13 NMR spectra, extraneous peaks in the NMR spectra due to different impurities and instrument noise, non-reproducible spectra due to the pulsed Fourier transform intervals and the nuclear Overhauser effect, impure samples often isolated as stereoisomeric mixtures or as mixed esters and superposition of peak signals in the NMR spectra due to carbons in the same environment within the same compound. These factors make identification by traditional computational and expert systems impossible. As a result of these shortcomings, the author has developed a novel approach using artificial neural network techniques. The artificial neural network system developed used real data from the 300 MHz NMR spectrometer in the Department of Chemistry, Durban. The system was trained to discriminate between limonoids, triterpenoids and flavonoids/coumarins from the C-13 NMR spectra of pure, impure and unseen compounds with an accuracy of better than 90%. Further differentiation of the glabretals from the rest of the protolimonoids as well as from the rest of the triterpenoids showed similarly significant results. Finally, individual limonoid discrimination within the limonoid dataset was extremely successful. Apart from its application to the extractives from Meliaceae, the methodology and techniques developed by the author can be applied to other sets of extractives to provide a robust method for the spectral classification of pre-identified natural products

Fuzzy and tile coding approximation techniques for coevolution in reinforcement learning

PhDThis thesis investigates reinforcement learning algorithms suitable for learning in large state space problems and coevolution. In order to learn in large state spaces, the state space must be collapsed to a computationally feasible size and then generalised about. This thesis presents two new implementations of the classic temporal difference (TD) reinforcement learning algorithm Sarsa that utilise fuzzy logic principles for approximation, FQ Sarsa and Fuzzy Sarsa. The effectiveness of these two fuzzy reinforcement learning algorithms is investigated in the context of an agent marketplace. It presents a practical investigation into the design of fuzzy membership functions and tile coding schemas. A critical analysis of the fuzzy algorithms to a related technique in function approximation, a coarse coding approach called tile coding is given in the context of three different simulation environments; the mountain-car problem, a predator/prey gridworld and an agent marketplace. A further comparison between Fuzzy Sarsa and tile coding in the context of the nonstationary environments of the agent marketplace and predator/prey gridworld is presented. This thesis shows that the Fuzzy Sarsa algorithm achieves a significant reduction of state space over traditional Sarsa, without loss of the finer detail that the FQ Sarsa algorithm experiences. It also shows that Fuzzy Sarsa and gradient descent Sarsa(λ) with tile coding learn similar levels of distinction against a stationary strategy. Finally, this thesis demonstrates that Fuzzy Sarsa performs better in a competitive multiagent domain than the tile coding solution