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The integration of a fuzzy system and automaton theory can form the concept of fuzzy automaton. This integration allows a discretely defined state-machine to act on continuous universes and handle uncertainty in applications like Intrusion Detection Systems (IDS). The typical IDS detection mechanisms are targeted to detect and prevent single-stage attacks. These types of attacks can be detected using either a common convincing threshold or by pre-defined rules. However, attack techniques have changed in recent years. Currently, the largest proportion of attacks performed, are multi-step attacks. The goal of this paper is to introduce a novel detection mechanism for multi-step attacks built upon Fuzzy Rule Interpolation (FRI) based fuzzy automaton. In that respect, the FRI method instruments the fuzzy automaton to be able to act on a not fully defined state transition rule-base, by offering interpolated conclusion even for situations which are not explicitly defined. In the suggested model, the intrusion definition state transition rule-base is defined using an open source fuzzy declarative language. On the multi-step attack benchmark dataset introduced in this paper, the proposed detection mechanism was able to achieve 97.836% detection rate. Furthermore, in the studied examples, the suggested method was able not only to detect but also early detect the multi-step attack in stages, where the planned attack is not fully elaborated and hence less harmful. According to these results, the IDS built upon the FRI based fuzzy automaton could be a useful device for detecting multi-step attacks, even in cases when the intrusion state transition rule-based is incomplete. The early detection of multi-step attacks also allows the administrator to take the necessary actions in time, to mitigate the potential threats.

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Abstract. The Fuzzy Rule Interpolation (FRI)-based Fuzzy Automaton is an efficient structure for describing complex behaviour models in a relatively simple manner. The goal of this paper is to introduce a novel declarative behaviour description language which is created for supporting special needs of ethologically inspired behaviour model definition. For the sake of simplicity, the grammar is created with as few keywords as possible, keeping the ability to describe complex behavioural patterns as well. The language is a declarative language mainly supporting the behaviour models built upon structures of interpolative fuzzy automata. The paper firstly presents the formal structure of the behaviour description language itself, then gives an overview of the interpreting and processing engine designed for the language. Finally, an application example, a definition of a set of behaviours and a simulated environment is also presented.