Evaluating Sequential Combination of Two Genetic Algorithm-Based Solutions for Intrusion Detection

The paper presents a serial combination of two genetic algorithm-based intrusion detection systems. Feature extraction techniques are deployed in order to reduce the amount of data that the system needs to process. The designed system is simple enough not to introduce significant computational overhead, but at the same time is accurate, adaptive and fast. There is a large number of existing solutions based on machine learning techniques, but most of them introduce high computational overhead. Moreover, due to 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, generating a solution competitive with the solutions of the state-of-the-art.