Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

THE DESIGN OF A POWER SYSTEM FOR A WIRELESS SENSOR NETWORK FOR LONG-TERM OPERATION

Wireless sensor networks (WSNs) have shown wide applicability to many fields including monitoring of environmental, civil, and industrial settings. WSNs however are resource constrained by many competing factors that span their hardware, software, and networking. One of the central resource constrains is the charge consumption of WSN nodes. With finite energy supplies, low charge consumption is needed to ensure long lifetimes and success of WSNs. This thesis details the design of a power system to support long-term operation of WSNs. The power system’s development occurs in parallel with a custom WSN from the Queen’s MEMS Lab (QML-WSN), with the goal of supporting a 1+ year lifetime without sacrificing functionality. The final power system design utilizes a TPS62740 DC-DC converter with AA alkaline batteries to efficiently supply the nodes while providing battery monitoring functionality and an expansion slot for future development. Testing tools for measuring current draw and charge consumption were created along with analysis and processing software. Through their use charge consumption of the power system was drastically lowered and issues in QML-WSN were identified and resolved including the proper shutdown of accelerometers, and incorrect microcontroller unit (MCU) power pin connection. Controlled current profiling revealed unexpected behaviour of nodes and detailed current-voltage relationships. These relationships were utilized with a lifetime projection model to estimate a lifetime between 521-551 days, depending on the mode of operation. The power system and QML-WSN were tested over a long term trial lasting 272+ days in an industrial testbed to monitor an air compressor pump. Environmental factors were found to influence the behaviour of nodes leading to increased charge consumption, while a node in an office setting was still operating at the conclusion of the trail. This agrees with the lifetime projection and gives a strong indication that a 1+ year lifetime is achievable. Additionally, a light-weight charge consumption model was developed which allows charge consumption information of nodes in a distributed WSN to be monitored. This model was tested in a laboratory setting demonstrating +95% accuracy for high packet reception rate WSNs across varying data rates, battery supply capacities, and runtimes up to full battery depletion.

One Approach to the Problem Solution of Specialized Software Development for Subject Search

In the article relevance of system development for subject search using computational linguistics is considered. The basic principles of system functioning are defined. The principle of grammar development for information retrieval from the partially structured text in a natural language is considered. The ranging principle of results of information search is defined.

SubCM: A tool for improved visibility of software change in an industrial setting

Software Configuration Management is the discipline of managing large collections of software development artefacts from which software products are built. Software configuration management tools typically deal with artefacts at fine levels of granularity - such as individual source code files - and assist with coordination of changes to such artefacts. This paper describes a lightweight tool, designed to be used on top of a traditional file-based configuration management system. The add-on tool support enables users to flexibly define new hierarchical views of product structure, independent of the underlying artefact-repository structure. The tool extracts configuration and change data with respect to the user-defined hierarchy, leading to improved visibility of how individual subsystems have changed. The approach yields a range of new capabilities for build managers, and verification and validation teams. The paper includes a description of our experience using the tool in an organization that builds large embedded software systems.

Applying network analysis to the conservation of habitat trees in urban environments: a case study from Brisbane, Australia

In Australia more than 300 vertebrates, including 43 insectivorous bat species, depend on hollows in habitat trees for shelter, with many species using a network of multiple trees as roosts, We used roost-switching data on white-striped freetail bats (Tadarida australis; Microchiroptera: Molossidae) to construct a network representation of day roosts in suburban Brisbane, Australia. Bats were caught from a communal roost tree with a roosting group of several hundred individuals and released with transmitters. Each roost used by the bats represented a node in the network, and the movements of bats between roosts formed the links between nodes. Despite differences in gender and reproductive stages, the bats exhibited the same behavior throughout three radiotelemetry periods and over 500 bat days of radio tracking: each roosted in separate roosts, switched roosts very infrequently, and associated with other bats only at the communal roost This network resembled a scale-free network in which the distribution of the number of links from each roost followed a power law. Despite being spread over a large geographic area (> 200 km(2)), each roost was connected to others by less than three links. One roost (the hub or communal roost) defined the architecture of the network because it had the most links. That the network showed scale-free properties has profound implications for the management of the habitat trees of this roosting group. Scale-free networks provide high tolerance against stochastic events such as random roost removals but are susceptible to the selective removal of hub nodes. Network analysis is a useful tool for understanding the structural organization of habitat tree usage and allows the informed judgment of the relative importance of individual trees and hence the derivation of appropriate management decisions, Conservation planners and managers should emphasize the differential importance of habitat trees and think of them as being analogous to vital service centers in human societies.

An FPGA network architecture for accelerating 3DES - CBC

This paper presents a DES/3DES core that will support cipher block chaining (CBC) and also has a built in keygen that together take up about 10% of the resources in a Xilinx Virtex II 1000-4. The core will achieve up to 200Mbit/s of encryption or decryption. Also presented is a network architecture that will allow these CBC capable 3DES cores to perform their processing in parallel.

A software audit framework for spyware risk mitigation

Our research described in this paper identifies a three part premise relating to the spyware paradigm. Firstly the data suggests spyware is proliferating at an exponential rate. Secondly ongoing research confirms that spyware produces many security risks – including that of privacy/confidentiality breaches via illicit data collection and reporting. Thirdly, anti-spyware controls are improving but are still considered problematic for several reasons. Our research then concludes that control measures to counter this very significant challenge should merit compliance auditing – and this auditing may effectively target the vital message passing performed by all illicit data collection spyware. Our research then evolves into an experiment involving the design and implementation of a software audit tool to conduct the desired compliance auditing. The software audit tool is positioned at the protected network’s gateway. The software audit tool uses ‘phone-home’ IP addresses as spyware signatures to detect the presence of the offending software. The audit tool also has the capability to differentiate legitimate message passing software from that produced by spyware – and ‘learn’ both new spyware signatures and new legitimate message passing profiles. The testing stage of the software has proven successful – albeit using very limited levels of network message passing variety and frequency.

Test strategies on functionally partitioned module-based programmable architecture for base-band processing

A specialised reconfigurable architecture for telecommunication base-band processing is augmented with testing resources. The routing network is linked via virtual wire hardware modules to reduce the area occupied by connecting buses. The number of switches within the routing matrices is also minimised, which increases throughput without sacrificing flexibility. The testing algorithm was developed to systematically search for faults in the processing modules and the flexible high-speed routing network within the architecture. The testing algorithm starts by scanning the externally addressable memory space and testing the master controller. The controller then tests every switch in the route-through switch matrix by making loops from the shared memory to each of the switches. The local switch matrix is also tested in the same way. Next the local memory is scanned. Finally, pre-defined test vectors are loaded into local memory to check the processing modules. This algorithm scans all possible paths within the interconnection network exhaustively and reports all faults. Strategies can be inserted to bypass minor faults

The time dimension of neural network models

This review attempts to provide an insightful perspective on the role of time within neural network models and the use of neural networks for problems involving time. The most commonly used neural network models are defined and explained giving mention to important technical issues but avoiding great detail. The relationship between recurrent and feedforward networks is emphasised, along with the distinctions in their practical and theoretical abilities. Some practical examples are discussed to illustrate the major issues concerning the application of neural networks to data with various types of temporal structure, and finally some highlights of current research on the more difficult types of problems are presented.

Mathematical modelling and optimal multivariable control of chemical processes

This work reports the developnent of a mathenatical model and distributed, multi variable computer-control for a pilot plant double-effect climbing-film evaporator. A distributed-parameter model of the plant has been developed and the time-domain model transformed into the Laplace domain. The model has been further transformed into an integral domain conforming to an algebraic ring of polynomials, to eliminate the transcendental terms which arise in the Laplace domain due to the distributed nature of the plant model. This has made possible the application of linear control theories to a set of linear-partial differential equations. The models obtained have well tracked the experimental results of the plant. A distributed-computer network has been interfaced with the plant to implement digital controllers in a hierarchical structure. A modern rnultivariable Wiener-Hopf controller has been applled to the plant model. The application has revealed a limitation condition that the plant matrix should be positive-definite along the infinite frequency axis. A new multi variable control theory has emerged fram this study, which avoids the above limitation. The controller has the structure of the modern Wiener-Hopf controller, but with a unique feature enabling a designer to specify the closed-loop poles in advance and to shape the sensitivity matrix as required. In this way, the method treats directly the interaction problems found in the chemical processes with good tracking and regulation performances. Though the ability of the analytical design methods to determine once and for all whether a given set of specifications can be met is one of its chief advantages over the conventional trial-and-error design procedures. However, one disadvantage that offsets to some degree the enormous advantages is the relatively complicated algebra that must be employed in working out all but the simplest problem. Mathematical algorithms and computer software have been developed to treat some of the mathematical operations defined over the integral domain, such as matrix fraction description, spectral factorization, the Bezout identity, and the general manipulation of polynomial matrices. Hence, the design problems of Wiener-Hopf type of controllers and other similar algebraic design methods can be easily solved.

The retromer coat complex coordinates endosomal sorting and dynein-mediated transport, with carrier recognition by the trans-Golgi network

Early endosome-to-trans-Golgi network (TGN) transport is organized by the retromer complex. Consisting of cargo-selective and membrane-bound subcomplexes, retromer coordinates sorting with membrane deformation and carrier formation. Here, we describe four mammalian retromers whose membrane-bound subcomplexes contain specific combinations of the sorting nexins (SNX), SNX1, SNX2, SNX5, and SNX6. We establish that retromer requires a dynamic spatial organization of the endosomal network, which is regulated through association of SNX5/SNX6 with the p150(glued) component of dynactin, an activator of the minus-end directed microtubule motor dynein; an association further defined through genetic studies in C. elegans. Finally, we also establish that the spatial organization of the retromer pathway is mediated through the association of SNX1 with the proposed TGN-localized tether Rab6-interacting protein-1. These interactions describe fundamental steps in retromer-mediated transport and establish that the spatial organization of the retromer network is a critical element required for efficient retromer-mediated sorting.

The design of fault tolerant software for loosely coupled distributed systems

Requirements for systems to continue to operate satisfactorily in the presence of faults has led to the development of techniques for the construction of fault tolerant software. This thesis addresses the problem of error detection and recovery in distributed systems which consist of a set of communicating sequential processes. A method is presented for the `a priori' design of conversations for this class of distributed system. Petri nets are used to represent the state and to solve state reachability problems for concurrent systems. The dynamic behaviour of the system can be characterised by a state-change table derived from the state reachability tree. Systematic conversation generation is possible by defining a closed boundary on any branch of the state-change table. By relating the state-change table to process attributes it ensures all necessary processes are included in the conversation. The method also ensures properly nested conversations. An implementation of the conversation scheme using the concurrent language occam is proposed. The structure of the conversation is defined using the special features of occam. The proposed implementation gives a structure which is independent of the application and is independent of the number of processes involved. Finally, the integrity of inter-process communications is investigated. The basic communication primitives used in message passing systems are seen to have deficiencies when applied to systems with safety implications. Using a Petri net model a boundary for a time-out mechanism is proposed which will increase the integrity of a system which involves inter-process communications.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.