Automatic Dataset Labelling and Feature Selection for Intrusion Detection Systems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Correctly labelled datasets are commonly required. Three particular scenarios are highlighted, which showcase this need. When using supervised Intrusion Detection Systems (IDSs), these systems need labelled datasets to be trained. Also, the real nature of the analysed datasets must be known when evaluating the efficiency of the IDSs when detecting intrusions. Another scenario is the use of feature selection that works only if the processed datasets are labelled. In normal conditions, collecting labelled datasets from real networks is impossible. Currently, datasets are mainly labelled by implementing off-line forensic analysis, which is impractical because it does not allow real-time implementation. We have developed a novel approach to automatically generate labelled network traffic datasets using an unsupervised anomaly based IDS. The resulting labelled datasets are subsets of the original unlabelled datasets. The labelled dataset is then processed using a Genetic Algorithm (GA) based approach, which performs the task of feature selection. The GA has been implemented to automatically provide the set of metrics that generate the most appropriate intrusion detection results

Three-body problem in a two-dimensional Fermi gas

We investigate the three-body properties of two identical "up" fermions and one distinguishable "down" atom interacting in a strongly confined two-dimensional geometry. We compute exactly the atom-dimer scattering properties and the three-body recombination rate as a function of collision energy and mass ratio m_up/m_down. We find that the recombination rate for fermions is strongly energy dependent, with significant contributions from higher partial waves at low energies. For m_up < m_down, the s-wave atom-dimer scattering below threshold is completely described by the scattering length. Furthermore, we examine the "up-up-down" bound states (trimers) appearing at large m_up/m_down and find that the energy spectrum for the deepest bound trimers resembles that of a hydrogen atom confined to two dimensions.Comment: 6 pages, 6 figure

Adding Contextual Information to Intrusion Detection Systems Using Fuzzy Cognitive Maps

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In the last few years there has been considerable increase in the efficiency of Intrusion Detection Systems (IDSs). However, networks are still the victim of attacks. As the complexity of these attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of IDSs should be designed incorporating reasoning engines supported by contextual information about the network, cognitive information and situational awareness to improve their detection results. In this paper, we propose the use of a Fuzzy Cognitive Map (FCM) in conjunction with an IDS to incorporate contextual information into the detection process. We have evaluated the use of FCMs to adjust the Basic Probability Assignment (BPA) values defined prior to the data fusion process, which is crucial for the IDS that we have developed. The experimental results that we present verify that FCMs can improve the efficiency of our IDS by reducing the number of false alarms, while not affecting the number of correct detections

Hints on sampling premium wheat crops

EACH year much of the premium wheat received by millers is inferior in quality to the samples on which the premium bids are based. This often leads to reduction in the premium paid. Cereal Products Adviser J. Parish, B.Sc. (Agric), describes how samples can overestimate the quality of a crop and suggests methods of taking samples which will give a true estimate of crop quality

Wheat quality in W.A

Three separate quality classes or categories of wheat are now received by C.B.H. These are a soft biscuit wheat, a hard bread wheat and a mixed grain suitable as a filler wheat in bread-making and for making noodles. In this article the basis of quality differences between these classes of wheat is described and some of the terms and concepts used in distinguishing between cultivars (varieties) and classes of wheat are discussed

Wheat quality surveys in Western Australia. 2. Variation between seasons and localities in the production of high and low protein wheat

Although the protein content of West Australian f.a.q. wheat averages only 9.4 per cent., often substantial amounts of comparatively high protein wheat are contained in this mixture. However the quantity varies widely from year to year and is obtained from widely scattered areas of the wheatbelt