A Genetic Algorithm-based Solution for Intrusion Detection

In this work we present a serial combination of two genetic algorithm-based intrusion detection systems. Many solutions for intrusion detection based on machine learning techniques have been proposed, but most of them introduce significant computational overhead, which makes them time-consuming and thus increases their period of adapting to the environmental changing. In the first step of our solution we deploy feature extraction techniques in order to reduce the amount of data that the system needs to process. Hence, our system is simple enough not to introduce significant computational overhead, but at the same time is accurate, adaptive and fast due to genetic algorithms. Furthermore, on account of its inherent parallelism, our solution offers a possibility of implementation using reconfigurable hardware with the implementation cost much lower than the one of the traditional systems. The model is verified on KDD99 benchmark dataset and it has been proven that it is comparable to the solutions of the state-of-the-art, while exhibiting the mentioned advantaged.