Session Program

Abstract - Fuzzy signature sets (FSigSets) are extensions of the original fuzzy set concept, and also of the Vector Valued Fuzzy Set notion. In a FSigSet rule base the (input) universe of discourse X is mapped into a set of hierarchically grouped fuzzy sets, and each element of X has a "membership degree" consisting of a rooted tree with membership degrees at each leaf and aggregations at the intermediate vertices. The structure of the tree is identical for each element in the case of homogenous FSigSets, and so are the aggregations, depending only on the position of the vertex. Interpolation in fuzzy rule bases allows the calculation of a conclusion in the output universe Y belonging to an observation even if there are gaps in the rule base and the observation does not intersect with any of the antecedent sets. The key question here is how to determine the degree of similarity, or inversely, the distance, of any observation from the surrounding antecedents of the rules in the base, so that the distance incorporates the information involved with the close connection of the features in the sub-groups, and the aggregations expressing the form of this connection. A solution is proposed, and a pair of numerical examples is presented.

Abstract - Fuzzy rule interpolation (FRI) provides an alternative means to make inference with a sparse rule base, rather than directly resulting in failed reasoning when no rules can be fired for an input observation. However, existing approaches to FRI typically assume that rule antecedents are of equal significance in the implementation of interpolation, thereby often leading to less accurate interpolated results. Having taken notice of feature selection (FS) techniques being capable of selecting (subsets of) informative features, providing a mechanism of evaluating and ranking features, this work employs FS to score the individual rule antecedents in a given rule base. In particular, the computation of individual scores is enabled by the introduction of an innovative reverse engineering technique that artificially creates a set of training samples from a given sparse rule base. The antecedent scores are integrated within the scale and move transformation-based FRI algorithm (though other FRI approaches may employ the same idea), forming a novel feature ranking-guided FRI method. The work is systematically examined, by utilising six different FS techniques and comparing over eight benchmark classification problems, demonstrating improved classification performance.

Abstract - Dynamic fuzzy rule interpolation (D-FRI) consists of functionalities of fuzzy rule interpolation and dynamically refinement of the fuzzy rule base. It can be integrated with any fuzzy intelligent system to extend the system's capabilities in addition to its normal fuzzy reasoning. Systems security is one of the areas that require dynamic monitoring due to the nature of possible threats; static rule-based systems cannot cover all reoriented security threats accurately in the long run. D-FRI provides a possible solution to such problems, potentially making various security tools (e.g., those for firewall, intrusion detection and traffic analysis) more effective. As a particular application, this paper exploits D-FRI to dynamically support Microsoft Windows Firewall, resulting in a robust system named D-FRIWinFirewall. Given the general utility of Windows Firewall, the impact of this work is ubiquitous. The work reported here focusses on the monitoring and prevention of denial of service (DoS) attacks, which is not possible by utilising the standard Windows Firewall alone. In particular, two sub-systems are designed, implemented and tested within D-FRI-WinFirewall, with an effort to monitor and prevent the two most severe types of DoS attack: ICMP DoS and UDP DoS, leading the Windows Firewall to outperform popular and expensive firewalls, which are yet unable to handle DoS attacks.

Abstract - Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated.

Abstract - The Quality of Services (QoS) is the measure of data transmission quality and service availability of a network, aiming to maintain the data, especially delay-sensitive data such as VoIP, to be transmitted over the network with the required quality. Major network device manufacturers have each developed their own smart dynamic QoS solutions, such as AutoQoS supported by Cisco, CoS (Class of Service) by Netgear devices, and QoS Maps on SROS (Secure Router Operating System) provided by HP, to maintain the service level of network traffic. Such smart QoS solutions usually only work for manufacture qualified devices and otherwise only a pre-defined static policy mapping can be applied. This paper presents a dynamic QoS solution based on the differentiated services (DiffServ) approach for enterprise networks, which is able to modify the priority level of a packet in real time by adjusting the value of Differentiated Services Code Point (DSCP) in Internet Protocol (IP) header of network packets. This is implemented by a 0-order TSK fuzzy model with a sparse rule base which is developed by considering the current network delay, application desired priority level and user current priority group. DSCP values are dynamically generated by the TSK fuzzy model and updated in real time. The proposed system has been evaluated in a real network environment with promising results generated.

Abstract - The T-CGMP inference scheme combines the GMP principles with additional constraints to guide inference in the case where the observation does not match the rule premise. This paper studies its exploitation in the case where several rules are available, in particular considering the case of two rules: it proposes a local variant of T-CGMP and examines its behaviour in this interpolation-like framework, highlighting its specific features, in particular the original uncertain outputs it produces to keep track of shape mismatches. The resulting inference scheme can thus be seen as an intermediary between GMP and interpolation.