A study on genetic-fuzzy based automatic intrusion detection on network datasets

The intrusion detection aims at distinguishing the attack data and the normal data from the network pattern database. It is an indispensable part of the information security system. Due to the variety of network data behaviors and the rapid development of attack fashions, it is necessary to develop a fast machine-learning-based intrusion detection algorithm with high detection rates and low false-alarm rates. In this correspondence, we propose a novel fuzzy method with genetic for detecting intrusion data from the network database. Genetic algorithm is an evolutionary optimization technique, which uses Directed graph structures instead of strings in genetic algorithm or trees in genetic programming, which leads to enhancing the representation ability with a compact programs derived from the reusability of nodes in a graph structure. By combining fuzzy set theory with Genetic proposes a new method that can deal with a mixed of database that contains both discrete and continuous attributes and also extract many important association rules to contribute and to enhance the Intrusion data detections ability. Therefore, the proposed method is flexible and can be applied for both misuse and anomaly detection in data-intrusion-detection problems. Also the incomplete database will include some of the missing data in some tuples and however, the proposed methods by applying some rules to extract these tuples. The Genetic-Fuzzy presents a data Intrusion Detection Systems for recovering data. It also include following steps in Genetic-Fuzzy rules: Process data model as a mathematical representation for Normal data.; Improving the process data model which improves the Model of normal data and it should represent the underlying truth of normal Data.; Uses cluster centers or centroids and use distances away from the centroids and co