Abstract: To solve the problem that brute-force attack poses a major risk to network security, this paper proposes an open source brute-force attack recognition algorithm for big data in cyberspace. The open source network space data information model is constructed, the set of parameter vectors is obtained, and the calculation results of model variables are optimized. Based on ant colony algorithm, the feature optimization is transformed into a path search problem. First, the brute-force attack feature is regarded as a location to be visited by ants, and then the state transition probability is selected to refine part of the search to obtain the global optimal feature. Using the information gain method to measure features, the gain of each feature information in the data set is obtained. By calculating the function of the values between single data sets, the sample difference is measured, the outlier value in the data set is reduced, and the attack behavior is identified by comparing with the threshold value. The experimental results show that the proposed algorithm can accurately identify the brute-force attack, the recognition rate is above 95% , the false positive rate is low, and the recognition effect is the best. 

Key words: open source network, spatial big data, chaotic synchronization method, ant colony, brute force attack, information gain, network attack identification, feature selection