Abstract: With the continuous progress of network technology, some abnormal data are often confused in network data flow, which affects network security. In order to grasp the abnormal degree of abnormal data in networks and detect the similarity of abnormal data, an optimized genetic data mining algorithm is used to mine abnormal data in network, obtain the initial population of abnormal data mining and optimize genetic operation. On this basis, the network data type and the number of network data types are adaptively adjusted to obtain the optimal abnormal data mining results. Based on Euclidean distance, the similarity value of abnormal data in network is calculated, and the greater the similarity value is, the greater the similarity of abnormal data is and vice versa. The experimental results show that the average standard deviation of detection error and energy consumption of the proposed method are 0.00865 and 398J, respectively. This method is a reliable and energy-saving method for similarity detection of abnormal data in network, which provides an effective basis for grasping the anomaly degree of network data.
Keywords: Data mining, abnormal data in network, population, optimized genetics, similarity, detection
