Any device connected to the Internet needs to be protected. Using a firewall as a first line of defence is a very common way to provide protection. A firewall can be set up to protect an entire network or just a single host. As it is becoming more and more popular to connect mobile phones and other hand held devices to the Internet, the big question is;"how to protect those devices from the perils of the Internet?" This work investigates issues with the implementation of a firewall system for protecting mobile devices. Firewall administration is an error prone and difficult task. Setting up a correctly configured firewall in a network setting is a difficult task for a network administrator. To enable an ordinary mobile phone user to set up a firewall configuration to protect his mobile phone it is important to have a system that is easy to understand and warns the user of possible mistakes. Generic algorithms for firewall rule-set sorting and anomaly discovery are presented. This ensures that the rule-set is error free and safe to use. This is a vital part of any firewall system. The prototype developed can be used to find errors in existing firewall rule-sets. The rule-set can be in either a native firewall configuration format (currently only IPF is supported) or in a generic XML format. This generic XML format was developed as a part of this research project. Further a new graphical visualization concept that allows the end user to configure an advanced firewall configuration from a device with a small screen and limited input possibilities is presented.

Thesis (MSc (Geography and Environmental Studies))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: Supervised classifiers are the most popular approach for image classification due to their high accuracies, ease of use and strong theoretical grounding. Their primary disadvantage is the high level of user input required during the creation of the data needed to train the classifier. One alternative to supervised classification is an expert-system rule-based approach where expert knowledge is used to create a set of rules which can be applied to multiple images. This research compared supervised and expert-system rule-based approaches for forest mapping. For this purpose two SPOT 5 images were acquired and atmospherically corrected. Field visits, aerial photography, high resolution imagery and expert forestry knowledge were used for the compilation of the training data and the development of a rule-set. Both approaches were evaluated in an object-orientated environment. It was found that the accuracy of the resulting maps was equivalent, with both techniques returning an overall classification accuracy of 90%. This suggests that cost-effectiveness is the decisive factor for determining which method is superior. Although the development of the rule-set was time-consuming and challenging, it did not require any training data. In contrast, the supervised approach required a large number of training areas for each image classified, which was time-consuming and costly. Significantly more training areas will be required when the technique is applied to large areas, especially when multiple images are used. It was concluded that the rule-set is more cost-effective when applied at regional scale, but it is not viable for mapping small areas. / AFRIKAANSE OPSOMMING: Gerigte klassifiseerders is die gewildste benadering tot beeldklassifikasie as gevolg van hulle hoë graad van akkuraatheid, maklike aanwending en kragtige teoretiese fundering. Die primere nadeel van gerigte klassifikasie is die hoë vlak van gebruikersinsette wat benodig word tydens die skepping van opleidingsdata. 'n Alternatief vir gerigte klassifikasie is 'n deskundige stelsel waarin ‘n reëlgebaseerde benadering gevolg word om deskundige kennis aan te wend vir die opstel van 'n stel reëls wat op meervoudige beelde toegepas kan word. Hierdie navorsing het gerigte en deskundige stelsel benaderings toegepas vir bosboukartering om die twee benaderings met mekaar te vergelyk. Vir dié doel is twee SPOT 5 beelde verkry en atmosferies gekorrigeer. Veldbesoeke, lugfotografie, hoë-resolusie beelde en deskundige bosboukennis is aangewend om opleidingsdata saam te stel en die stel reëls te ontwikkel. Beide benaderings is in 'n objekgeoriënteerde omgewing beoordeel. Die akkuraatheidsvlakke van die resulterende kaarte was ewe hoog vir beide tegnieke met 'n algehele klassifikasie-akkuraatheid van 90%. Dit wil dus voorkom asof koste-effektiwiteit eerder as akkuraatheid die deurslaggewende faktor is om te bepaal watter metode die beste is. Alhoewel die ontwikkeling van die stel reëls tydrowend en uitdagend was, het dit geen opleidingsdata vereis nie. In teenstelling hiermee is 'n groot aantal opleidingsgebiede geskep vir elke beeld wat met gerigte klassifikasie verwerk is – 'n tydrowende en duur opsie. Dit is duidelik dat meer opleidingsgebiede benodig sal word wanneer die tegniek op groot gebiede toegepas word, veral omdat meervoudige beelde gebruik sal word. Gevolglik sal die stel reëls meer kosteeffektief wees wanneer dit op streekskaal toegepas word. ‘n Deskundige stelsel benadering is egter nie lewensvatbaar vir die kartering van klein gebiede nie.

Machine learning approaches have been successfully applied to many classification and prediction problems. One of the most popular machine learning approaches is decision trees. A main advantage of decision trees is the clarity of the decision model they produce. The ID3 algorithm proposed by Quinlan forms the basis for many of the decision trees’ application. Trees produced by ID3 are sensitive to small perturbations in training data. To overcome this problem and to handle data uncertainties and spurious precision in data, fuzzy ID3 integrated fuzzy set theory and ideas from fuzzy logic with ID3. Several fuzzy decision trees algorithms and tools exist. However, existing tools are slow, produce a large number of rules and/or lack the support for automatic fuzzification of input data. These limitations make those tools unsuitable for a variety of applications including those with many features and real time ones such as intrusion detection. In addition, the large number of rules produced by these tools renders the generated decision model un-interpretable. In this research work, we proposed an improved version of the fuzzy ID3 algorithm. We also introduced a new method for reducing the number of fuzzy rules generated by Fuzzy ID3. In addition we applied fuzzy decision trees to the classification of real and pseudo microRNA precursors. Our experimental results showed that our improved fuzzy ID3 can achieve better classification accuracy and is more efficient than the original fuzzy ID3 algorithm, and that fuzzy decision trees can outperform several existing machine learning algorithms on a wide variety of datasets. In addition our experiments showed that our developed fuzzy rule reduction method resulted in a significant reduction in the number of produced rules, consequently, improving the produced decision model comprehensibility and reducing the fuzzy decision tree execution time. This reduction in the number of rules was accompanied with a slight improvement in the classification accuracy of the resulting fuzzy decision tree. In addition, when applied to the microRNA prediction problem, fuzzy decision tree achieved better results than other machine learning approaches applied to the same problem including Random Forest, C4.5, SVM and Knn.

Quality has become a central concern as it has been observed that reducing defects will lower the cost of production. Hence, companies generate and store vast amounts of quality related data. Analysis of this data is critical in order to understand the quality problems and their causes, and to take preventive actions. In this thesis, we propose a methodology for this analysis based on one of the data mining techniques, association rules. The methodology is applied for quality related data of an electronics company. Apriori algorithm used in this application generates an excessively large number of rules most of which are redundant. Therefore we implement a three phase elimination process on the generated rules to come up with a reasonably small set of interesting rules. The approach is applied for two different data sets of the company, one for production defects and one for raw material non-conformities. We then validate the resultant rules using a test data set for each problem type and analyze the final set of rules.

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Paketklassifikation ist eine Kernfunktionalität vieler Netzwerksysteme, wie zum Beispiel Firewalls und SDN-Switches. Für viele dieser Systeme ist Durchsatz von höchster Bedeutung. Weitere wichtige Eigenschaften sind dynamische Aktualisierbarkeit und hohe Regelsatz-Ausdrucksfähigkeit. Die Kombination dieser Eigenschaften macht Paketklassifikation zu einem schwierigen Problem. Diese Arbeit befasst sich mit dem Design von Klassifikationssystemen und -algorithmen, welche mindestens zwei dieser Eigenschaften vereinen. Es werden hybride Systeme sowie Systemspezialisierung verwendet, um effiziente Ansätze zum Paketklassifikationsproblem in drei Bereichen zu erarbeiten: Klassifikationsalgorithmen, Regelsatztransformation und hardwarebasierte Architekturen. Die Beiträge im Bereich der Klassifikationsalgorithmen sind Jit Vector Search (JVS) und das SFL-System. JVS verbessert existierende Techniken durch spezialisierte Suchdatenstrukturen und durch Nutzung von SIMD-Fähigkeiten der CPU, was in fast optimaler Klassifikationsperformanz bei kaum erhöhten Vorberechnungszeiten resultiert. Das hybride SFL-System hingegen kombiniert einen Klassifikationsalgorithmus mit einem Änderungspuffer, um sowohl hohe Klassifikations- als auch Aktualisierungsperformanz zu ermöglichen. Bezüglich Regelsatztransformationen wird die RuleBender-Technik vorgestellt, welche Suchbäume in Regelsätze für Firewalls mit Sprungsemantik kodiert. Somit kann der Durchsatz dieser Systeme unter Beibehaltung komplexer Regelsatzsemantik um eine Größenordnung gesteigert werden. Schließlich wird der MPFC-Ansatz vorgestellt, welcher einen Regelsatz in einen auf einem FPGA implementierbaren Matching-Schaltkreis übersetzt. Die generierten Schaltkreise sind hochoptimiert und kleiner als generische Matching-Schaltkreise. Um dynamische Regelsatzänderungen zu ermöglichen, wird der hybride Consul-Ansatz konzipiert, welcher MPFC-Matcher mit generischen Matching-Schaltkreisen kombiniert. / Packet classification is a core functionality of a wide variety of network systems, such as firewalls and SDN switches. For many of these systems, throughput is of paramount importance. Further important system traits are dynamic updateability and high expressiveness in terms of rule set semantics. The combination of several of these properties turns packet classification into a hard problem. This work focuses on the design of classification systems and algorithms that combine at least two of the abovementioned characteristics. To this end, the concepts of hybrid systems and system specialization are employed to obtain efficient approaches to the packet classification problem in three domains: classification algorithms, rule set transformation, and hardware-centric architectures. The contributions in the domain of classification algorithms are Jit Vector Search (JVS) and the SFL system. JVS improves upon existing techniques through specialized search data structures and by exploiting SIMD capabilities of the underlying CPU, which results in near-optimal classification performance at only slightly increased preprocessing times. In contrast, the SFL system is a hybrid approach that combines a classification algorithm with an update buffer to allow for high classification as well as update performance. With respect to rule set transformation, the RuleBender technique is proposed, which encodes search tree structures into rule sets of firewalls with jump semantics. That way, the throughput of these systems can be improved by an order of magnitude, while maintaining complex matching semantics. Finally, the MPFC approach is proposed, which translates a given rule set into a matching circuit that can be implemented on an FPGA. The generated circuits are highly optimized and significantly smaller than those of generic matchers. To allow for dynamic rule set updates, the hybrid Consul approach is devised, which combines MPFC circuits with a generic matcher.